Voror_Wiki - User contributions [en-gb]

Glossary

2022-12-02T09:47:30Z

SimonMeredith:

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary. Please let us know if you would like to challenge or add definitions. simon.meredith@nhs.net or jack.barker@nhs.net
{| class="wikitable sortable"
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!Term
!Definition
!External Links
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|Canonical Data Model
|This is a type of data model that presents data entities and relationships in the simplest possible form. It is a design pattern which allows communication between different data formats. ''Canonical'' form describes the simplest representation of an object.
|[[wikipedia:Canonical_model|Canonical model - Wikipedia]]
|-
|Categories
|Broad sections of an Ontology or Classification such as the Categories in ICD 10 which contain all ICD 10 codes related to one body part. It might be also be large groupings of test results e.g. Haematology, Biochemistry, Immunology, Microbiology etc.
|[https://icd.who.int/browse10/2010/en#/II ICD 10]
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|[[wikipedia:Cloud_computing|Cloud Computing]]
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|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|[[wikipedia:Cloud_native_computing|Cloud Native Computing]]
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|Cluster
|A group of concept codes making another idea. The Cluster may be made up of concepts from different ontologies or classifications e.g., Snomed CT and ICD10. Synonyms for Cluster are refset, value set, codelist, code set, grouper. The name "cluster" was strongly advocated for by John Robson ([https://www.qmul.ac.uk/blizard/ceg/ Clinical Effectiveness Group] (CEG) NEL) because of his work in primary care.
|[http://app.powerbi.com/view?r=eyJrIjoiZTY0ODY0YzEtMjhhYy00ZTViLWJjZDQtMWZjOWVkMTZlOGExIiwidCI6IjUwZjYwNzFmLWJiZmUtNDAxYS04ODAzLTY3Mzc0OGU2MjllMiIsImMiOjh9 Primary Care Domain Reference Set Portal]
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|Common Data Model
|This is the shared Data Model which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
It defines the transformation of data from multiple different databases with potentially multiple coding systems into a common storage format (data model) as well as a common representation (terminologies, vocabularies, coding schemes)
|[https://www.jamesserra.com/archive/2019/06/common-data-model/ Microsoft Example]
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|Common Data Model (Discovery)
|The Discovery Common Data Model is the schema of the Discovery Compass v2 databases which exist in the Discovery Core and are held as subscriber databases within ICS environments. The schema is [https://wiki.discoverydataservice.org/index.php?title=Remote_Subscriber_Database_(RSD)_Schema_(Compass_2) here]..... The schema is thought to be based on EMIS primary care system schema.
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|Compass Database (Compass v2)
|The database schema used in "subscriber" databases in DDS. This is often referred to as the DDS Common Data Model. There are currently 3 versions of the Compass database within the Discovery AWS core, one for each ICS - NEL, SEL and NWL. These are still referred to as "Subscriber" databases by original DDS management team. There are four "Compass" databases outside the AWS core. One with NEL CEG, one with NEL ICS, one with SEL ICS and one in NWL WSIC infrastructure.
|[[Compass 2 Schema Mappings]]
[[Compass Database Mappings]]
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|Concept
|A coded idea. For example, Name = Baker's Asthma, SNOMED CT code = 34015007
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|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
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|Customer Managed Keys
|Customer Managed Keys are security keys that are created, owned, and managed by the customer. The customer has full control over enabling, disabling, key policies, rotating cryptographic material, scheduling for deletion. In simple terms, this means a supplier may hold the customer's data in their system (software as a service, for example) which is encrypted by the customer managed key. The supplier is unable to decrypt the data. Only the customer can do so as they hold the key.
|[https://docs.aws.amazon.com/kms/latest/developerguide/concepts.html#customer-cmk Customer Managed Keys - AWS]
[https://learn.microsoft.com/en-us/azure/security/fundamentals/key-management Key Management in Azure]
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|Data Dictionary
|A centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format e.g. NHS Data Dictionary.
|[[wikipedia:Data_dictionary|Data Dictionary]]
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|Data Mart
|A data mart is a subset of a data warehouse focused on a particular line of business, department, or subject area. Data marts make specific data available to a defined group of users, which allows those users to quickly access critical insights without wasting time searching through an entire data warehouse
|[https://www.ibm.com/cloud/learn/data-mart#:~:text=A%20data%20mart%20is%20a,through%20an%20entire%20data%20warehouse. Data Mart]
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|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|[[wikipedia:Data_model|Data Model]]
[https://digital.nhs.uk/services/nhs-data-model-and-dictionary-service NHS Data Model and Dictionary Service]
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|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
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|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
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|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
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|[https://ico.org.uk/for-organisations/guide-to-data-protection/ico-codes-of-practice/data-sharing-a-code-of-practice/data-sharing-agreements/ Data Sharing Agreement] (DSA) Framework
|The arrangements for storing and managing data sharing agreements to ensure transparency across controllers and processors. It is likely that the Data Usage Committees/Data Access Groups ([https://ico.org.uk/for-organisations/guide-to-data-protection/key-dp-themes/guidance-for-the-use-of-personal-data-in-political-campaigning-1/controllers-joint-controllers-and-processors/ Joint Controllers]) will generate and store a copy of the DSA, whilst processors and sub-processors will also need a copy.
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|Data Warehouse
|A data warehouse stores data for the main purpose of reporting and data analysis. They are central repositories which bring together disparate data sets into one place, to enable reporting at scale and pace.
|[[wikipedia:Data_warehouse|Data Warehouse]]
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|Dataset
|A '''data set''' (or '''dataset''') is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question. The data set lists values for each of the variables, such as for example height and weight of an object, for each member of the data set. Data sets can also consist of a collection of documents or files.

In the Discovery Data Service datasets may be generated as a result of queries and valueset filters on the internal relational database.
|[[wikipedia:Data_set|Dataset]]
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|Discovery Collaborative
|A collaboration of three integrated care systems (ICS) with the ambition to develop and maintain a data service. It arose out of NE London where GPs and academics were working with a charitable trust to develop a secure, trusted, aggregated information resource to support quality improvement and research. As part of the national Local Health and Care Record Exemplar programme it expanded to NW and SE London ICSs. The Collaborative has a Board and working groups. The Chair of the Board reports to the Senior Responsible Officer for digital in each of the three ICSs.
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|Discovery Data Service (DDS)
|The hardware and software made available by the Discovery Collaborative. The DDS is not an organisation. The management and development of the bulk of the DDS is currently sub-contracted to a private company Voror in an Amazon Web Service Environment. However, there have been discussions about in-housing the service under closer control by NEL CCG and moving the service to a Microsoft Azure environment.
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|Discovery Health Information Model
|The "toolkit " of the Discovery Data Service. 1) A set of ontologies and classifications 2) A common data model 3) A metadata library of clusters of codes (codesets, valuesets etc) - currently the IM1 data tables in the DDS that are managed through IM2 (see below) 4) A catalogue of reference data (such as geographical areas, organisations and people). 5) A library of queries 6) A set of “maps” (mappings) 7) An Information Manager - currently (Endeavour Information Manager 2 - IM2) - an open source set of utilities that can be used to browse, search, or maintain the model.
|[https://wiki.endeavourhealth.org/index.php?title=Discovery_health_information_model Discovery Health Information Model]
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|Endeavour Information (Model) Manager
|The Information Model Manager is an application developed by the Endeavour Charitable Trust which provides a view of the Common Information Model and supports authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|[https://im.endeavourhealth.net/#/ Endeavour IM2]
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|Environment (computing)
|An environment is the collection of computer machinery, data storage devices, work stations, software applications, and networks that support the processing and exchange of electronic information required by the software application. Typically there will be several environments for one system, often including a development environment (where developers can test out new code and see how it interacts), a staging, or test, environment (where new code is subject to an automated test suite comprising end-to-end tests, regression testing, unit and integration tests, as well as manual User Acceptance Testing), and a production environment (which is where the live application runs).
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|Expression (Snomed CT)
|A combination of concepts that represent something that is not already represented as a Concept or Cluster. It typically links things across concepts (disease of infection (finding), with bacteria name (organism), with sepsis (observable entity) with kidney (body structure).
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/SNOMED+CT+expression Snomed CT Expression]
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|Expression Constraint Language
|The ''Expression Constraint Language'' is a formal syntax for representing SNOMED CT expression constraints. Expression constraints are computable rules used to define a bounded sets of clinical meanings represented by either precoordinated or postcoordinated expressions. Expression constraints can be used to restrict the valid values for a data element in an EHR, as the intensional definition of a concept-based reference set, as a machine processable query that identifies a set of matching expressions, or as a constraint that restricts the range of an attribute defined in the SNOMED CT concept model.
|Expression Constraint [https://confluence.ihtsdotools.org/display/DOCECL Language]
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|Facts
|Patient "Facts" - A concept introduced by NEL CSU to include '''1) Clinical components''' - effectively cohorts or registers - for instance long term conditions with national (e.g. QOF) or locally defined definitions e.g. Hypertension, Asthma '''2) Segmentation models''' e.g. "frailty" or likelihood of admission. '''3) Care quality standard compliance''' - quality measures which identify whether care standards are being met i.e. has a medication review or health check been done? Has the patient had each of 8 care processes for diabetes? Has the patient on a hypertension register had a blood pressure scheck and met the target?
Developing these at a London scale might be an excellent way to promote standardised Population Health Management.
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|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
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|Functional Requirements
|These are product features or functionality that must be developed in order for the system to satisfy requirements. They must be implemented in the system in order for it to achieve what it is supposed to accomplish. Examples include "users should be able to download data based on their filtered search criteria" or "only admin-level users should be able to reset passwords for other users"
|[[wikipedia:Functional_requirement|Functional requirement]]
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|GitHub
|GitHub is a provider of version control and source-code management using [[wikipedia:Git|Git]] (which is software used for tracking changes in files). It enables software developers to safely store source code and work on it collaboratively, merging changes from different developers into the main "production" branch safely. It provides additional features such as bug/issue tracking, continuous integration, feature requests etc. GitHub was acquired by Microsoft in 2018.
|[[wikipedia:GitHub|GitHub]]
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|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
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|HDRUK Phenotype Library
|This is a comprehensive, open access resource which exists to provide the research community with information, tools, and phenotyping algorithms for UK electronic health records (EHRs)
|[https://phenotypes.healthdatagateway.org/ HDR UK Phenotype Library]
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|Information Model
|A model describing ontologically standardised data items following a specific database schema within a specific relational database architecture.
It is an abstract model, showing the constraints, relationships, concepts, and data items for a domain.

The Discovery Information Model is a representation of the meaning and structure of data held in the electronic records of the health and social care sector. It includes libraries of queries, value sets, concept sets, data set definitions and mappings. The main purpose is to bridge the chasm that exists between highly technical digital representations and plain language so that when questions are asked of data, a lay person could use plain language without prior knowledge of the underlying models.
|[[wikipedia:Information_model|Information Model]]

[[Discovery health information model|DDS IM]]

[https://im.endeavourhealth.net/#/ IM Viewer]

[https://blog.softwaresuperglue.com/2018/11/09/information-model-vs-data-model/ Information Model vs Data Model]
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|Integrated Care Board (ICB)
|The ICB is a legal entity created in the Health and Care Act 2022. ICBs will bring the NHS together locally to deliver shared priorities, with a greater emphasis on collaboration and shared responsibility for the health of the local population. This will require governance arrangements that support collective accountability between partner organisations for whole-system delivery and performance. These arrangements should be proportionate, and they must facilitate transparent decision-making and foster the culture and behaviours that enable system working.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
[https://www.legislation.gov.uk/ukpga/2022/31/contents/enacted Health and Care Act 2022]
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|Integrated Care Partnership (ICP)
|The ICP is a joint committee of the ICB and the upper tier local authorities that are wholly or partly in the ICB area. From 1 July, the ICB and the local authorities will be under a legal duty to establish the ICP.
It is the role of the ICP to develop and publish the integrated care strategy for the ICB area, in particular focusing on how health and care can better integrate.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
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|Integrated Care System (ICS)
|Integrated care systems (ICSs) are partnerships of health and care organisations that come together to plan and deliver joined up services and to improve the health of people who live and work in their area.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
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|Joint Controller Agreement (JCA)
|The arrangements by which a group of data controllers agree to work together to agree the uses of data and how they jointly take responsibility for that. This is frequently manifested as the development of a representative committee that reviews the five safes, the legal basis for use of personal data, the worthiness of the proposed use of data
|[https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/controllers-and-processors/what-does-it-mean-if-you-are-joint-controllers/ Joint Controllers]
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|Knowledge Graph
|An information architecture where concepts are represented as nodes and edges in a network of relationships. This is usually represented in a NoSQL architecture designed to around relationships between concepts.
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|Level 2 - the London Health Data Service (LHDS)
|The name is derived from the Local Health and Care Exemplar Level 2 work. It now refers to the data service hosted by NE London ICB in their Microsoft Azure environment. This data service is now called The London Health Data Service. In the first instance it will contain close to real time primary care accessed using IM1 feeds, in addition to commissioning data sets that have been processed by NHS Digital. In time it is hoped that it will process real-time HL7 messages from provider organisations, unstructured data, imaging and "'omics". It is intended that it will be a part of the London sub-National Secure Data Environment for Research and Development (SN SDE for R+D). In phase 1 of the SN SDE for R+D the LHDS will send data to the NWL Discover-Now architecture, which, in turn, will act as a Trusted Research Environment (TRE) for London. The hope is that the LHDS would be available for multiple other purposes including individual (direct) care, population health management, public health and commissioning. LHDS is also an acronym signifiying the London Health Data Strategy.
|[https://digital.nhs.uk/binaries/content/assets/website-assets/services/future-gp-it-systems/im1factsheetfeb2021.docx IM1]
[https://digital.nhs.uk/services/data-services-for-commissioners/commissioning-datasets commissioning data sets]

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|[[Logical Data Model]]
|The logical data model (or logical schema) describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|[[wikipedia:Logical_schema|Logical Schema]]
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|London Care Record
|A shared care record used for individual care. It is based on Cerner Health Information Exchange architecture.
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|Mauro Data Mapper
|Mauro Data Mapper is a third-party toolkit for the design and documentation of databases, data flows, and data standards, as well as related software artefacts such as data schemas and data forms. It was originally developed for the description of data in clinical research.
|[https://maurodatamapper.github.io/about/introduction/ Mauro Data Mapper]
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|Medical Classification
|Medical Classifications transform descriptions of procedures or diagnoses into standardised codes through the process of clinical coding. E.g., ICD10, OPCS4, LOINC. These
can also be called code sets. Hence, we call them a medical classification to avoid confusion with Clusters
|[[wikipedia:Medical_classification|Medical Classification]]
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|Metadata Library
|A tool that allows management of clusters
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|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|[https://digital.nhs.uk/services/terminology-servers NHSD Terminology Server]
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|Non-functional Requirements
|These are requirements concerning the operation of a system rather than functionality, and typically relate to security, scalability, performance, reliability etc. Examples include "the page must load within 500ms" or "the website must conform to WCAG AA accessibility standards"
|[[wikipedia:Non-functional_requirement|Non-functional Requirements]]
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|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|[https://www.imohealth.com/ideas/article/data-normalization-working-toward-a-common-clinical-terminology/ Healthcare Normalisation]
[https://blog.clinicalarchitecture.com/what-is-data-normalization What is data normalisation?]
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|Normalisation (database)
|This is the process of structuring a relational database in accordance with a series of derived rules, called ''normal forms'', in order to reduce redundancy, duplication, and improve data integrity.
|[[wikipedia:Database_normalization|Normalisation]]
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|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
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|OMOP Data Model
|OMOP stands for Observational Medical Outcomes Partnership, which was formed to inform the appropriate use of observational healthcare databases. OHDSI (Observational Health Data Sciences and Informatics) is a collaborative that now includes all of the original OMOP research investigators and will continue to develop tools using the OMOP common data model and vocabulary (OMOP is no longer and active programme). OMOP is cited in the UK Health Data Research paper on building Building (federated )[https://zenodo.org/record/5767586/files/211208%20Building%20TREs%20Paper%20v1.0.pdf Trusted Research Environments] (p22).
|[https://www.ohdsi.org/data-standardization/the-common-data-model/ OMOP]
[https://github.com/OHDSI/CommonDataModel/blob/v5.4.0/inst/ddl/5.4/sql_server/OMOPCDM_sql_server_5.4_ddl.sql OMOP Common Data Model (GitHub)]
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|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
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|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. The most currently used ontology is SNOMED CT - the DDS ontology is made up of several ontologies (such as READ, CTV3 etc) mapped to SNOMED where appropriate SNOMED terms already exist, and extended where they don't.
A representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse (e.g., medicine). Example: Snomed CT.
|[[wikipedia:Ontology_(information_science)|Ontology]]
[https://termbrowser.nhs.uk/ NHSD SNOMED browser]

[https://www.snomed.org/ SNOMED.org]

[https://www.england.nhs.uk/digitaltechnology/digital-primary-care/snomed-ct/ SNOMED - NHS England]
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|OpenCodelists
|OpenCodelists was created by OpenSAFELY for creating and sharing codelists. See "Cluster" definition above.
|[https://www.opencodelists.org/ OpenCodelists]
[https://www.opensafely.org/ OpenSAFELY]
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|PESTLE Analysis
|A PESTLE analysis studies the key external factors (Political, Economic, Sociological, Technological, Legal and Environmental) that influence an organisation. It can be used in a range of different scenarios, and can guide people professionals and senior managers in strategic decision-making
|[https://www.cipd.co.uk/knowledge/strategy/organisational-development/pestle-analysis-factsheet Pestle Analysis]
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|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|[[wikipedia:Physical_schema|Physical Schema]]
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|Primary Care Reference Set
|This is a cluster of codes used within business rules authored and maintained by NHSD's primary care domain.
|[https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/quality-and-outcomes-framework-qof/quality-and-outcome-framework-qof-business-rules/primary-care-domain-reference-set-portal NHSD Primary Care Domain Reference Set Portal]
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|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
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|SNOMED CT Concept
|A clinical idea to which a unique concept identifier has been assigned.
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/concept SNOMED CT Concept]
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|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|[[wikipedia:SQL|Structured Query Language]]
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|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
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|Star Schema
|A simple database schema which consists of fact tables and dimension tables
|[[wikipedia:Star_schema|Star schema]]
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|Subscription (Azure)
|An Azure Subscription is linked to a payment setup, and each subscription results in a separate bill. Several subscriptions can be owned by one tenant. Under each subscription, you can add resources, such as virtual machines, storage, networks etc.
|[https://www.parallels.com/blogs/ras/azure-subscription/#:~:text=An%20Azure%20subscription%20is%20a,are%20used%20and%20billed%20together. Azure Subscription] 
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|Tenancy/Tenant (Azure)
|An Azure tenant represents an organisation. It is a dedicated instance of Azure Active Directory (Azure AD - which is an enterprise identity service which holds all the user accounts, and provides role-based access, single sign-on, multifactor authentication etc ensuring only the right people have access to the right resources, only when they need it). Each Azure tenant is distinct and separate from other Azure AD tenants. It should not be confused with an Azure Subscription
|[https://docs.microsoft.com/en-us/azure/active-directory/fundamentals/active-directory-whatis Azure Active Directory] 
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|Trusted Data Environment
|Raw [health and care] data is not powerful on its own. It must be shaped, checked, and curated into shape. It must be housed, and managed securely. It must be analysed. And then it must be communicated, and acted upon. That work all requires people, with modern data skills, in teams, using platforms that protect patients’ privacy and avoid needless duplication of effort."
|[https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1067053/goldacre-review-using-health-data-for-research-and-analysis.pdf Better, Broader, Safer: Using Health Data for Research and Analysis]
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|Trusted Research Environment
|An approach to data access based primarily around Trusted (Trustworthy) Research Environments (TREs), a type of Secure Data Environment; with
appropriate robust and independent accreditation, monitoring and auditing.
|[https://zenodo.org/record/5767586/files/211208%20Building%20TREs%20Paper%20v1.0.pdf Building Trusted Research Environments. Principles and Best Practices; towards TRE ecosystems.]
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|Value Set
|A generic term for a set of values, or codes, which might be stored in a metadata library or other set of lookup data. Value sets are used as the filters of data to create Data Sets - for example, in the case of populating the London Care Record from DDS, Value Sets are used to filter the underlying data in order to create a Data Set which is suitable for ingestion.
Concepts and Clusters are types of Value Set.

Code Set is sometimes used interchangeably with Value Set
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|}

Glossary

2022-12-02T08:45:52Z

SimonMeredith: added data mart, customer managed keys

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary. Please let us know if you would like to challenge or add definitions. simon.meredith@nhs.net or jack.barker@nhs.net
{| class="wikitable sortable"
|+
!Term
!Definition
!External Links
|-
|Canonical Data Model
|This is a type of data model that presents data entities and relationships in the simplest possible form. It is a design pattern which allows communication between different data formats. ''Canonical'' form describes the simplest representation of an object.
|[[wikipedia:Canonical_model|Canonical model - Wikipedia]]
|-
|Categories
|Broad sections of an Ontology or Classification such as the Categories in ICD 10 which contain all ICD 10 codes related to one body part. It might be also be large groupings of test results e.g. Haematology, Biochemistry, Immunology, Microbiology etc.
|[https://icd.who.int/browse10/2010/en#/II ICD 10]
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|[[wikipedia:Cloud_computing|Cloud Computing]]
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|[[wikipedia:Cloud_native_computing|Cloud Native Computing]]
|-
|Cluster
|A group of concept codes making another idea. The Cluster may be made up of concepts from different ontologies or classifications e.g., Snomed CT and ICD10. Synonyms for Cluster are refset, value set, codelist, code set, grouper. The name "cluster" was strongly advocated for by John Robson ([https://www.qmul.ac.uk/blizard/ceg/ Clinical Effectiveness Group] (CEG) NEL) because of his work in primary care.
|[http://app.powerbi.com/view?r=eyJrIjoiZTY0ODY0YzEtMjhhYy00ZTViLWJjZDQtMWZjOWVkMTZlOGExIiwidCI6IjUwZjYwNzFmLWJiZmUtNDAxYS04ODAzLTY3Mzc0OGU2MjllMiIsImMiOjh9 Primary Care Domain Reference Set Portal]
|-
|Common Data Model
|This is the shared Data Model which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
It defines the transformation of data from multiple different databases with potentially multiple coding systems into a common storage format (data model) as well as a common representation (terminologies, vocabularies, coding schemes)
|[https://www.jamesserra.com/archive/2019/06/common-data-model/ Microsoft Example]
|-
|Common Data Model (Discovery)
|The Discovery Common Data Model is the schema of the Discovery Compass v2 databases which exist in the Discovery Core and are held as subscriber databases within ICS environments. The schema is [https://wiki.discoverydataservice.org/index.php?title=Remote_Subscriber_Database_(RSD)_Schema_(Compass_2) here]..... The schema is thought to be based on EMIS primary care system schema.
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|Compass Database (Compass v2)
|The database schema used in "subscriber" databases in DDS. This is often referred to as the DDS Common Data Model. There are currently 3 versions of the Compass database within the Discovery AWS core, one for each ICS - NEL, SEL and NWL. These are still referred to as "Subscriber" databases by original DDS management team. There are four "Compass" databases outside the AWS core. One with NEL CEG, one with NEL ICS, one with SEL ICS and one in NWL WSIC infrastructure.
|[[Compass 2 Schema Mappings]]
[[Compass Database Mappings]]
|-
|Concept
|A coded idea. For example, Name = Baker's Asthma, SNOMED CT code = 34015007
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|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
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|-
|Customer Managed Keys
|Customer Managed Keys are security keys that are created, owned, and managed by the customer. The customer has full control over enabling, disabling, key policies, rotating cryptographic material, scheduling for deletion. In simple terms, this means a supplier may hold the customer's data in their system (software as a service, for example) which is encrypted by the customer managed key. The supplier is unable to decrypt the data. Only the customer can do so as they hold the key.
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|-
|Data Dictionary
|A centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format e.g. NHS Data Dictionary.
|[[wikipedia:Data_dictionary|Data Dictionary]]
|-
|Data Mart
|A data mart is a subset of a data warehouse focused on a particular line of business, department, or subject area. Data marts make specific data available to a defined group of users, which allows those users to quickly access critical insights without wasting time searching through an entire data warehouse
|[https://www.ibm.com/cloud/learn/data-mart#:~:text=A%20data%20mart%20is%20a,through%20an%20entire%20data%20warehouse. Data Mart]
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|[[wikipedia:Data_model|Data Model]]
[https://digital.nhs.uk/services/nhs-data-model-and-dictionary-service NHS Data Model and Dictionary Service]
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
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|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
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|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
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|[https://ico.org.uk/for-organisations/guide-to-data-protection/ico-codes-of-practice/data-sharing-a-code-of-practice/data-sharing-agreements/ Data Sharing Agreement] (DSA) Framework
|The arrangements for storing and managing data sharing agreements to ensure transparency across controllers and processors. It is likely that the Data Usage Committees/Data Access Groups ([https://ico.org.uk/for-organisations/guide-to-data-protection/key-dp-themes/guidance-for-the-use-of-personal-data-in-political-campaigning-1/controllers-joint-controllers-and-processors/ Joint Controllers]) will generate and store a copy of the DSA, whilst processors and sub-processors will also need a copy.
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|Data Warehouse
|A data warehouse stores data for the main purpose of reporting and data analysis. They are central repositories which bring together disparate data sets into one place, to enable reporting at scale and pace.
|[[wikipedia:Data_warehouse|Data Warehouse]]
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|Dataset
|A '''data set''' (or '''dataset''') is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question. The data set lists values for each of the variables, such as for example height and weight of an object, for each member of the data set. Data sets can also consist of a collection of documents or files.

In the Discovery Data Service datasets may be generated as a result of queries and valueset filters on the internal relational database.
|[[wikipedia:Data_set|Dataset]]
|-
|Discovery Collaborative
|A collaboration of three integrated care systems (ICS) with the ambition to develop and maintain a data service. It arose out of NE London where GPs and academics were working with a charitable trust to develop a secure, trusted, aggregated information resource to support quality improvement and research. As part of the national Local Health and Care Record Exemplar programme it expanded to NW and SE London ICSs. The Collaborative has a Board and working groups. The Chair of the Board reports to the Senior Responsible Officer for digital in each of the three ICSs.
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|Discovery Data Service (DDS)
|The hardware and software made available by the Discovery Collaborative. The DDS is not an organisation. The management and development of the bulk of the DDS is currently sub-contracted to a private company Voror in an Amazon Web Service Environment. However, there have been discussions about in-housing the service under closer control by NEL CCG and moving the service to a Microsoft Azure environment.
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|Discovery Health Information Model
|The "toolkit " of the Discovery Data Service. 1) A set of ontologies and classifications 2) A common data model 3) A metadata library of clusters of codes (codesets, valuesets etc) - currently the IM1 data tables in the DDS that are managed through IM2 (see below) 4) A catalogue of reference data (such as geographical areas, organisations and people). 5) A library of queries 6) A set of “maps” (mappings) 7) An Information Manager - currently (Endeavour Information Manager 2 - IM2) - an open source set of utilities that can be used to browse, search, or maintain the model.
|[https://wiki.endeavourhealth.org/index.php?title=Discovery_health_information_model Discovery Health Information Model]
|-
|Endeavour Information (Model) Manager
|The Information Model Manager is an application developed by the Endeavour Charitable Trust which provides a view of the Common Information Model and supports authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|[https://im.endeavourhealth.net/#/ Endeavour IM2]
|-
|Environment (computing)
|An environment is the collection of computer machinery, data storage devices, work stations, software applications, and networks that support the processing and exchange of electronic information required by the software application. Typically there will be several environments for one system, often including a development environment (where developers can test out new code and see how it interacts), a staging, or test, environment (where new code is subject to an automated test suite comprising end-to-end tests, regression testing, unit and integration tests, as well as manual User Acceptance Testing), and a production environment (which is where the live application runs).
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|Expression (Snomed CT)
|A combination of concepts that represent something that is not already represented as a Concept or Cluster. It typically links things across concepts (disease of infection (finding), with bacteria name (organism), with sepsis (observable entity) with kidney (body structure).
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/SNOMED+CT+expression Snomed CT Expression]
|-
|Expression Constraint Language
|The ''Expression Constraint Language'' is a formal syntax for representing SNOMED CT expression constraints. Expression constraints are computable rules used to define a bounded sets of clinical meanings represented by either precoordinated or postcoordinated expressions. Expression constraints can be used to restrict the valid values for a data element in an EHR, as the intensional definition of a concept-based reference set, as a machine processable query that identifies a set of matching expressions, or as a constraint that restricts the range of an attribute defined in the SNOMED CT concept model.
|Expression Constraint [https://confluence.ihtsdotools.org/display/DOCECL Language]
|-
|Facts
|Patient "Facts" - A concept introduced by NEL CSU to include '''1) Clinical components''' - effectively cohorts or registers - for instance long term conditions with national (e.g. QOF) or locally defined definitions e.g. Hypertension, Asthma '''2) Segmentation models''' e.g. "frailty" or likelihood of admission. '''3) Care quality standard compliance''' - quality measures which identify whether care standards are being met i.e. has a medication review or health check been done? Has the patient had each of 8 care processes for diabetes? Has the patient on a hypertension register had a blood pressure scheck and met the target?
Developing these at a London scale might be an excellent way to promote standardised Population Health Management.
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|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
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|Functional Requirements
|These are product features or functionality that must be developed in order for the system to satisfy requirements. They must be implemented in the system in order for it to achieve what it is supposed to accomplish. Examples include "users should be able to download data based on their filtered search criteria" or "only admin-level users should be able to reset passwords for other users"
|[[wikipedia:Functional_requirement|Functional requirement]]
|-
|GitHub
|GitHub is a provider of version control and source-code management using [[wikipedia:Git|Git]] (which is software used for tracking changes in files). It enables software developers to safely store source code and work on it collaboratively, merging changes from different developers into the main "production" branch safely. It provides additional features such as bug/issue tracking, continuous integration, feature requests etc. GitHub was acquired by Microsoft in 2018.
|[[wikipedia:GitHub|GitHub]]
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
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|HDRUK Phenotype Library
|This is a comprehensive, open access resource which exists to provide the research community with information, tools, and phenotyping algorithms for UK electronic health records (EHRs)
|[https://phenotypes.healthdatagateway.org/ HDR UK Phenotype Library]
|-
|Information Model
|A model describing ontologically standardised data items following a specific database schema within a specific relational database architecture.
It is an abstract model, showing the constraints, relationships, concepts, and data items for a domain.

The Discovery Information Model is a representation of the meaning and structure of data held in the electronic records of the health and social care sector. It includes libraries of queries, value sets, concept sets, data set definitions and mappings. The main purpose is to bridge the chasm that exists between highly technical digital representations and plain language so that when questions are asked of data, a lay person could use plain language without prior knowledge of the underlying models.
|[[wikipedia:Information_model|Information Model]]

[[Discovery health information model|DDS IM]]

[https://im.endeavourhealth.net/#/ IM Viewer]

[https://blog.softwaresuperglue.com/2018/11/09/information-model-vs-data-model/ Information Model vs Data Model]
|-
|Integrated Care Board (ICB)
|The ICB is a legal entity created in the Health and Care Act 2022. ICBs will bring the NHS together locally to deliver shared priorities, with a greater emphasis on collaboration and shared responsibility for the health of the local population. This will require governance arrangements that support collective accountability between partner organisations for whole-system delivery and performance. These arrangements should be proportionate, and they must facilitate transparent decision-making and foster the culture and behaviours that enable system working.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
[https://www.legislation.gov.uk/ukpga/2022/31/contents/enacted Health and Care Act 2022]
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|Integrated Care Partnership (ICP)
|The ICP is a joint committee of the ICB and the upper tier local authorities that are wholly or partly in the ICB area. From 1 July, the ICB and the local authorities will be under a legal duty to establish the ICP.
It is the role of the ICP to develop and publish the integrated care strategy for the ICB area, in particular focusing on how health and care can better integrate.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
|-
|Integrated Care System (ICS)
|Integrated care systems (ICSs) are partnerships of health and care organisations that come together to plan and deliver joined up services and to improve the health of people who live and work in their area.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
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|Joint Controller Agreement (JCA)
|The arrangements by which a group of data controllers agree to work together to agree the uses of data and how they jointly take responsibility for that. This is frequently manifested as the development of a representative committee that reviews the five safes, the legal basis for use of personal data, the worthiness of the proposed use of data
|[https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/controllers-and-processors/what-does-it-mean-if-you-are-joint-controllers/ Joint Controllers]
|-
|Knowledge Graph
|An information architecture where concepts are represented as nodes and edges in a network of relationships. This is usually represented in a NoSQL architecture designed to around relationships between concepts.
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|Level 2 - the London Health Data Service (LHDS)
|The name is derived from the Local Health and Care Exemplar Level 2 work. It now refers to the data service hosted by NE London ICB in their Microsoft Azure environment. This data service is now called The London Health Data Service. In the first instance it will contain close to real time primary care accessed using IM1 feeds, in addition to commissioning data sets that have been processed by NHS Digital. In time it is hoped that it will process real-time HL7 messages from provider organisations, unstructured data, imaging and "'omics". It is intended that it will be a part of the London sub-National Secure Data Environment for Research and Development (SN SDE for R+D). In phase 1 of the SN SDE for R+D the LHDS will send data to the NWL Discover-Now architecture, which, in turn, will act as a Trusted Research Environment (TRE) for London. The hope is that the LHDS would be available for multiple other purposes including individual (direct) care, population health management, public health and commissioning. LHDS is also an acronym signifiying the London Health Data Strategy.
|[https://digital.nhs.uk/binaries/content/assets/website-assets/services/future-gp-it-systems/im1factsheetfeb2021.docx IM1]
[https://digital.nhs.uk/services/data-services-for-commissioners/commissioning-datasets commissioning data sets]

|-
|[[Logical Data Model]]
|The logical data model (or logical schema) describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|[[wikipedia:Logical_schema|Logical Schema]]
|-
|London Care Record
|A shared care record used for individual care. It is based on Cerner Health Information Exchange architecture.
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|Mauro Data Mapper
|Mauro Data Mapper is a third-party toolkit for the design and documentation of databases, data flows, and data standards, as well as related software artefacts such as data schemas and data forms. It was originally developed for the description of data in clinical research.
|[https://maurodatamapper.github.io/about/introduction/ Mauro Data Mapper]
|-
|Medical Classification
|Medical Classifications transform descriptions of procedures or diagnoses into standardised codes through the process of clinical coding. E.g., ICD10, OPCS4, LOINC. These
can also be called code sets. Hence, we call them a medical classification to avoid confusion with Clusters
|[[wikipedia:Medical_classification|Medical Classification]]
|-
|Metadata Library
|A tool that allows management of clusters
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|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|[https://digital.nhs.uk/services/terminology-servers NHSD Terminology Server]
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|Non-functional Requirements
|These are requirements concerning the operation of a system rather than functionality, and typically relate to security, scalability, performance, reliability etc. Examples include "the page must load within 500ms" or "the website must conform to WCAG AA accessibility standards"
|[[wikipedia:Non-functional_requirement|Non-functional Requirements]]
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|[https://www.imohealth.com/ideas/article/data-normalization-working-toward-a-common-clinical-terminology/ Healthcare Normalisation]
[https://blog.clinicalarchitecture.com/what-is-data-normalization What is data normalisation?]
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|Normalisation (database)
|This is the process of structuring a relational database in accordance with a series of derived rules, called ''normal forms'', in order to reduce redundancy, duplication, and improve data integrity.
|[[wikipedia:Database_normalization|Normalisation]]
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|
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|OMOP Data Model
|OMOP stands for Observational Medical Outcomes Partnership, which was formed to inform the appropriate use of observational healthcare databases. OHDSI (Observational Health Data Sciences and Informatics) is a collaborative that now includes all of the original OMOP research investigators and will continue to develop tools using the OMOP common data model and vocabulary (OMOP is no longer and active programme). OMOP is cited in the UK Health Data Research paper on building Building (federated )[https://zenodo.org/record/5767586/files/211208%20Building%20TREs%20Paper%20v1.0.pdf Trusted Research Environments] (p22).
|[https://www.ohdsi.org/data-standardization/the-common-data-model/ OMOP]
[https://github.com/OHDSI/CommonDataModel/blob/v5.4.0/inst/ddl/5.4/sql_server/OMOPCDM_sql_server_5.4_ddl.sql OMOP Common Data Model (GitHub)]
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. The most currently used ontology is SNOMED CT - the DDS ontology is made up of several ontologies (such as READ, CTV3 etc) mapped to SNOMED where appropriate SNOMED terms already exist, and extended where they don't.
A representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse (e.g., medicine). Example: Snomed CT.
|[[wikipedia:Ontology_(information_science)|Ontology]]
[https://termbrowser.nhs.uk/ NHSD SNOMED browser]

[https://www.snomed.org/ SNOMED.org]

[https://www.england.nhs.uk/digitaltechnology/digital-primary-care/snomed-ct/ SNOMED - NHS England]
|-
|OpenCodelists
|OpenCodelists was created by OpenSAFELY for creating and sharing codelists. See "Cluster" definition above.
|[https://www.opencodelists.org/ OpenCodelists]
[https://www.opensafely.org/ OpenSAFELY]
|-
|PESTLE Analysis
|A PESTLE analysis studies the key external factors (Political, Economic, Sociological, Technological, Legal and Environmental) that influence an organisation. It can be used in a range of different scenarios, and can guide people professionals and senior managers in strategic decision-making
|[https://www.cipd.co.uk/knowledge/strategy/organisational-development/pestle-analysis-factsheet Pestle Analysis]
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|[[wikipedia:Physical_schema|Physical Schema]]
|-
|Primary Care Reference Set
|This is a cluster of codes used within business rules authored and maintained by NHSD's primary care domain.
|[https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/quality-and-outcomes-framework-qof/quality-and-outcome-framework-qof-business-rules/primary-care-domain-reference-set-portal NHSD Primary Care Domain Reference Set Portal]
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|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
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|SNOMED CT Concept
|A clinical idea to which a unique concept identifier has been assigned.
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/concept SNOMED CT Concept]
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|[[wikipedia:SQL|Structured Query Language]]
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|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|
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|Star Schema
|A simple database schema which consists of fact tables and dimension tables
|[[wikipedia:Star_schema|Star schema]]
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|Subscription (Azure)
|An Azure Subscription is linked to a payment setup, and each subscription results in a separate bill. Several subscriptions can be owned by one tenant. Under each subscription, you can add resources, such as virtual machines, storage, networks etc.
|[https://www.parallels.com/blogs/ras/azure-subscription/#:~:text=An%20Azure%20subscription%20is%20a,are%20used%20and%20billed%20together. Azure Subscription] 
|-
|Tenancy/Tenant (Azure)
|An Azure tenant represents an organisation. It is a dedicated instance of Azure Active Directory (Azure AD - which is an enterprise identity service which holds all the user accounts, and provides role-based access, single sign-on, multifactor authentication etc ensuring only the right people have access to the right resources, only when they need it). Each Azure tenant is distinct and separate from other Azure AD tenants. It should not be confused with an Azure Subscription
|[https://docs.microsoft.com/en-us/azure/active-directory/fundamentals/active-directory-whatis Azure Active Directory] 
|-
|Trusted Data Environment
|Raw [health and care] data is not powerful on its own. It must be shaped, checked, and curated into shape. It must be housed, and managed securely. It must be analysed. And then it must be communicated, and acted upon. That work all requires people, with modern data skills, in teams, using platforms that protect patients’ privacy and avoid needless duplication of effort."
|[https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1067053/goldacre-review-using-health-data-for-research-and-analysis.pdf Better, Broader, Safer: Using Health Data for Research and Analysis]
|-
|Trusted Research Environment
|An approach to data access based primarily around Trusted (Trustworthy) Research Environments (TREs), a type of Secure Data Environment; with
appropriate robust and independent accreditation, monitoring and auditing.
|[https://zenodo.org/record/5767586/files/211208%20Building%20TREs%20Paper%20v1.0.pdf Building Trusted Research Environments. Principles and Best Practices; towards TRE ecosystems.]
|-
|Value Set
|A generic term for a set of values, or codes, which might be stored in a metadata library or other set of lookup data. Value sets are used as the filters of data to create Data Sets - for example, in the case of populating the London Care Record from DDS, Value Sets are used to filter the underlying data in order to create a Data Set which is suitable for ingestion.
Concepts and Clusters are types of Value Set.

Code Set is sometimes used interchangeably with Value Set
|
|}

Publisher API HL7 Segments

2022-10-27T09:04:01Z

SimonMeredith: Changes to PID:18 from NWL to ensure NWL use with Millennium sources is documented and the actual relationship with PV1:19 is shown

== Introduction ==
Below are all of the segments that a DDS Publisher using the HL7 API can send. The definitions below are generic and hence are relatively permissive. In the context of a specific message the segment various segment definitions may be refined to take account of the data needed to complete the business function associated with the message. More detail can be found on the [[Publisher API HL7 Messages|HL7 API messages page]]

== Coded fields ==
Note that currently only a subset of the coded fields that are specified in the segments below are interpreted by DDS (see below). The remainder are left as-is once ingested in to DDS are are not processed further. In this latter case it is recommended that publishers take note of the suggested code sets as in the future it might be that these fields are interpreted by DDS and therefore a mapping exercise will need to be undertaken to map from publisher local codes to a core DDS code set.

* Primary Language - PID:15
* Religion - PID:17
* Ethnic Group - PID:22
* Admission Type - PV1:4
* Hospital Service - PV1:10
* Admit Source - PV1:14
* Patient Type - PV1:18
* Discharge Disposition - PV1:36
* Discharged to Location - PV1:37

== MSA - Message acknowledgement ==

=== Overview ===
The MSA segment contains information sent whilst acknowledging an inbound message. The following rules govern the nature of the acknowledgement -

* If the message could not be saved to the DDS message store then an AR (failure occurred, retry) acknowledgement code is returned.
* If the message is malformed, or fails sender, recipient or message type checks, or is missing a message control ID, or fails for an unexpected reason, an AE (failure occurred, move to next message) acknowledgement code is returned.
* If the message passes the above checks and is saved to the message store, an AA (success) acknowledgement code is returned.
* If the message receiver fails to send an acknowledgement (e.g. network outage, hardware failure etc), it is expected the sender will automatically re attempt to send the message.

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|MSA:1 - Acknowledgement code
|MSH:1.1
|ID
|R
|N
|Message type. Allowed values are from table [[Publisher API Code Sets#DDS-HL7v2-AckCode|DDS-HL7v2-AckCode]]
|
|-
|MSA:2 - Message control ID
|MSH:1.1
|ST
|R
|N
|Unique (to DDS) identifier assigned by DDS
|
|-
|-
|MSA:3 - Text message
|MSA:3.1
|ST
|O
|N
|Further describes an error condition (may not always be provided by DDS)
|
|}

== MSH - Message Header ==

=== Overview ===
The MSH segment defines the intent, source, destination, and some specifics of the syntax of a message

=== Code Meanings ===
{| class="wikitable"
|code
|meaning
|-
|O
|optional
|-
|R
|required
|-
|TS
|time stamp
|-
|ST
|string
|-
|CX
|extended composite id with check digit
|-
|N
|no repeats
|-
|R
|multiple repeats
|}

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|MSH.1 - Field Separator
|MSH:1.1
|ST
|R
|N
|Defines the character to be used as the separator in the message
|
|-
|MSH.2 - Encoding characters
|MSH:2.1
|ST
|R
|N
|Contains (in the following order) component separator, repetition separator, escape character, and subcomponent separator
|
|-
|MSH:3 - Sending application
|MSH:3.1
|ST
|R
|N
|Sending application name
|
|-
|MSH:4 - Sending facility
|MSH:4.1
|ST
|R
|N
|Sending facility name
|
|-
|MSH:5 - Receiving application
|MSH:5.1
|ST
|R
|N
|Receiving application name
|
|-
|MSH:6 - Receiving facility
|MSH:6.1
|ST
|R
|N
|Receiving facility name
|
|-
|MSH:7 - Message timestamp
|MSH:7.1
|DT
|R
|N
|Datetime that the sending system created the message
|
|-
|-
| rowspan="3" |MSH:9 - Message type
| -
| -
|R
|N
|
|
|-
|MSH:9.1
|ID
|R
|N
|Message type. Values SHOULD be from table [[Publisher API Code Sets#DDS-HL7v2-MessageType|DDS-HL7v2-MessageType]]
|
|-
|MSH:9.2
|ID
|R
|N
|Trigger event. Values SHOULD be from table [[Publisher API Code Sets#DDS-HL7v2-EventType|DDS-HL7v2-EventType]]
|
|-
|MSH:10 - Message control ID.
|MSH:10.1
|ST
|R
|N
|This must be unique for all messages sent to DDS by each publisher. However, DDS does not currently detect or reject duplicate messages based on this value, so it is important that each publisher enforces this themselves.
|
|-
|MSH:11 - Processing ID
|MSH:11.1
|PT
|O
|N
|In production MUST be "P"
|
|-
|MSH:12 - Message version
|MSH:12.1
|ID
|R
|N
|The version of HL7 that this message conforms to. MUST be "2.3"
|
|}

== EVN - Event Type ==

=== Overview ===
The EVN segment is used to communicate trigger event information to receiving applications

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|EVN:1 - Event Type Code
|EVN:1.1
|ID
|O
|N
|The value should be the same as theMSH:9.2 (trigger event)
|
|-
|EVN:2 - Recorded Date/Time
|EVN:2.1
|TS
|R
|N
|Timestamp of when the transaction was entered
|
|-
|EVN:3 - Date/Time planned event
|EVN:3.1
|TS
|O
|N
|Avoid populating this field. Instead use PV2 expected admit date and PV2 expected discharge date whenever possible.
|
|-
|EVN:4 - Event reason code
|EVN:4.1
|CWE
|O
|N
|The reason for this event. Allowed values are from table [[Publisher API Code Sets#0062-EventReason|0062-EventReason]]
|
|-
| rowspan="5" |EVN:5 - Operator ID
| -
| -
|O
|N
|Operator ID. If provided, at least the family name must be given.
|
|-
|EVN:5.2
|ST
|R
|N
|Family Name
|
|-
|EVN:5.3
|ST
|O
|N
|Given Name
|
|-
|EVN:5.4
|ST
|O
|N
|Middle Names
|
|-
|EVN:5.6
|ST
|O
|N
|Prefix
|
|-
|EVN:6 - Event occured
|EVN:6.1
|TS
|R
|N
|This field contains the date/time that the event actually occurred. For example, on a transfer (A02 (transfer a patient)), this field must contain the date/time the patient was actually transferred. On a cancellation event, this field should contain the date/time that the event being cancelled occurred.
|
|}

== PID - Patient Identification ==

=== Overview ===
The PID segment is used as the main way of communicating patient identification information. The majority of patient identifying and demographic information held within the PID segment is not subject to frequent changes

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|PID:2 - Patient ID
| -
| -
|X
|N
|Not used. Instead please populate PID.3 Patient Identifier List
|
|-
| rowspan="3" |PID:3 - Patient Identifier List
| -
| -
|R
|Y
|Patient Identifier List to include

Always:

* the patient's main local (to the publisher) identifier

Optionally:

* alternative MRNs
* the patient's NHS number
|
|-
|PID:3.1
|ST
|R
|Y
|Patient ID
|
|-
|PID:3.4
|ST
|R
|Y
|Assigning authority

* "MRN" - for the patient's main local (to the publisher) identifier
* "ALT_MRN" - for alternative MRNs
* "NHS" - for NHS Number
|<nowiki>PID|1| |01529577^^^MRN~4111111998^^^NHS~E02055831^^^ALT_MRN~M2600001^^^ALT_MRN</nowiki>
|-
| rowspan="5" |PID:5 - Patient Name
| -
| -
|R
|N
|
|
|-
|PID:5.1
|ST
|R
|N
|Family name
|
|-
|PID:5.2
|ST
|O
|N
|Given name
|
|-
|PID:5.3
|ST
|O
|N
|Middle names
|
|-
|PID:5.5
|ST
|O
|N
|Title
|
|-
|PID:7
|PID:7.1
|TS
|R
|N
|Date of birth
|
|-
|PID:8
|PID:8.1
|IS
|R
|N
|Administrative sex. If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from table [[Publisher API Code Sets#AdministrativeSex|AdministrativeSex]]
|
|-
| rowspan="8" |PID:11 - Patient Address
| -
| -
|O
|N
|
|
|-
|PID:11.1
|ST
|O
|N
|Address line 1
|
|-
|PID:11.2
|ST
|O
|N
|Address line 2
|
|-
|PID:11.3
|ST
|O
|N
|City
|
|-
|PID:11.5
|ST
|O
|N
|Post code
|
|-
|PID:11.6
|ID
|O
|N
|Country. Values SHOULD be from table [[Publisher API Code Sets#0399-CountryCode|0399-CountryCode]]
|
|-
|PID:11.7
|ID
|O
|N
|Address usage eg temporary address
|
|-
|PID:11.9
|IS
|O
|N
|County
|
|-
| rowspan="3" |PID:13 - Home contact information
| -
| -
|O
|N
|
|
|-
|PID:13.1
|TN
|O
|N
|Contact value
|
|-
|PID:13.2
|ID
|O
|N
|Contact use code. Allowed values are "home" or "mobile"
|
|-
| rowspan="3" |PID:14 - Work contact information
| -
| -
|O
|N
|
|
|-
|PID:14.1
|TN
|O
|N
|Contact value
|
|-
|PID:14.2
|ID
|O
|N
|Contact use code. Allowed values is "work"
|
|-
| rowspan="2" |PID:15 - Primary language
| -
| -
|O
|N
|Primary language. This should be the ISO 639-1 code or one of the five communication method extensions defined in the NHS Data Dictionary code set.
|
|-
|PID:15.1
|ST
|O
|N
|Primary language. If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source]<nowiki> then allowed values are child concepts of the SNOMED CT World Languages concept - 297289008 | World languages (qualifier value) |</nowiki>
|
|-
|PID:17 - Religion
|PID:17.1
|IS
|O
|N
|Religion. If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from [https://datadictionary.nhs.uk/attributes/religious_or_other_belief_system_affiliation_code.html RELIGIOUS OR OTHER BELIEF SYSTEM AFFILIATION CODE]
|
|-
|PID:18 - Patient Account Number
|PID:18.1
|ST
|C
|N
|This field is used by DDS as the identifier for the episode of care if the PV1.19 field is not populated. If the PV1.19 field is populated then PID:18 may be locally repurposed (eg. at NWL it is used to supply HIE with the FIN number for de-duplication of Millennium sourced visits) An episode of care encapsulates all of the encounters that a patient has with a healthcare organisation from the point of referral to the point of discharge. If no PV1 segment accompanies this PID segment then PID:18 should not be populated
|Example 1 (PV1 present and PID:18 populated with fin number):

MSH|^~\&|CERNER|RQM|DISCOVERY|COMMUNITY|20220706152033||ADT^A01|Q1128671561T1226605750X164711A1148|P|2.3

EVN|A01|20220706151800|||^^^^^^^^^|20220706151800

PID|||90092146^^^MRN^MRN~9438453504^^^NHS^NH{status:01}||YYYCWTEST^TESTIMUPDATE^INTERFACE^^^^CURRENT||19801020|M|||Address line 1^^LONDON^^^GBR^HOME^||^||5|M|JUDAISM|61920590^^^RQM Encounter Num^FINNBR||||G||||||DZA||

PD1|||^^|^^^^^^^^

PV1||I|CW DEV^Bay B^Bed 04^CWH^^BED^CW MAIN|22|...

Example 2 (PV1 absent, so PID:18 unpopulated)

MSH|^~\&|CERNER|RQM|DISCOVERY|COMMUNITY|20220706093029||ADT^A28|Q1128670316T1226604112X162958A1148|P|2.3

EVN|A28|20220706093029|||^^^^^^^^^|20220706093029

PID|||90092136^^^MRN^MRN||YYYCWTEST^TESTIM^TESTING^^^^CURRENT||19851015|M|||Address line 1^^^^^GBR^HOME^||^|||M|CHRISTIAN|||||P||||||||

PD1|||...
|-
|PID:22 - Ethnic group
| -
| -
|O
|N
|
|
|-
|
|PID:22.1
|ST
|O
|N
|Ethnicity identifier. If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from [https://datadictionary.nhs.uk/data_elements/ethnic_category.html?hl=ethnicity ETHNIC CATEGORY]
|
|-
|PID:29 - Patient Death Date and Time
|PID:29.1
|TS
|C
|N
|If PID.30:1 is not empty and PID:30.1 == "Y" then PID:29 is mandatory
|
|-
|PID:30 - Patient Death Indicator
|PID:30.1
|ID
|O
|N
|"Y" or "N"
|
|}

== PD1 - Patient Additional Demographic ==

=== Overview ===
The PD1 segment contains demographic information about a patient that is likely to change. In the case of DDS is it used to carry GP data.

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
| rowspan="3" |PD1:3 - Patient Primary Facility
| -
| -
|R
|N
|
|
|-
|PD1:3.1
|ST
|R
|N
|name
|
|-
|PD1:3.3
|ST
|R
|N
|ODS code
|
|-
| rowspan="5" |PD1:4 - Patient Primary Care Provider Name and ID No
| -
| -
|R
|N
|
|
|-
|PD1:4.1
|ST
|R
|N
|GMC code
|
|-
|PD1:4.2
|FN
|R
|N
|Family name
|
|-
|PD1:4.3
|ST
|R
|N
|Given name
|
|-
|PD1:4.6
|ST
|O
|N
|Prefix
|
|}
== MRG - Merge Patient Information ==

=== Overview ===
The MRG segment is used by DDS to support merging of patient identifiers

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|MRG:1 - Prior Patient Identifier List
| -
| -
| -
| -
|Patient Identifier List to include

Always

* the patient's main local (to the publisher) identifier

Optionally

* alternative MRNs
* the patient's NHS number
|
|-
|
|MRG:1.1
|CX
|R
|Y
|The patient's identifier
|
|-
|
|MRG:1.4
|CX
|R
|N
|Assigning authority

* “MRN” – for the patient's main local (to the publisher) identifier
* “ALT_MRN” – for alternative MRNs
* “NHS” for NHS number
|
|-
|MRG:2 - Prior Alternate Patient ID
|
|CX
|X
|N
|Ignored if supplied
|
|-
|MRG:3 - Prior Alternate Account Number
|
|CX
|X
|N
|Ignored if supplied
|
|-
|MRG:4 - Prior Patient ID
|
| -
|X
|N
|Ignored if supplied
|
|-
|MRG:5 - Prior Visit Number
|
|CX
|X
|N
|Ignored if supplied
|
|-
|MRG:6 - Prior Alternate Visit ID
|
|CX
|X
|N
|Ignored if supplied
|
|-
|MRG:7 - Prior Patient Name
| -
| XPN
|X
|N
|Ignored if supplied
|
|} 

== PV1 - Patient Visit ==

=== Overview ===
The PV1 segment is used by Registration/Patient Administration applications to communicate information on a visit-specific basis.

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|PV1:2 - Patient Class
|PV1:2.1
|ID
|R
|N
|If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from table [[Publisher API Code Sets#PatientClass|PatientClass]]
|
|-
|PV1:3 - Assigned Patient Location
|PV1:3.1
|IS
|R
|N
|General patient location
|
|-
|PV1:4 - Admission Type
|PV1:4.1
|ID
|O
|N
|If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from [https://datadictionary.nhs.uk/attributes/admission_method.html ADMISSION METHOD]
|
|-
| rowspan="5" |PV1:8 - Referring Doctor
| -
| -
|O
|N
|Referring doctor. If provided, at least the family name must be given.
|
|-
|PV1:8.1
|ST
|O
|N
|ID number
|
|-
|PV1:8.2
|ST
|O
|N
|Family Name
|
|-
|PV1:8.3
|ST
|O
|N
|Given Name
|
|-
|PV1:8.6
|ST
|O
|N
|Prefix
|
|-
| rowspan="5" |PV1:9 - Consulting Doctor
| -
| -
|R
|N
|Consulting doctor. If provided, at least the family name must be given.
|
|-
|PV1:9.1
|ST
|R
|N
|ID number
|
|-
|PV1:9.2
|ST
|R
|N
|Family Name
|
|-
|PV1:9.3
|ST
|O
|N
|Given Name
|
|-
|PV1:9.6
|ST
|O
|N
|Prefix
|
|-
|PV1:10 - Hospital Service
|PV1:10.1
|IS
|R
|N
|If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from [https://datadictionary.nhs.uk/attributes/treatment_function_code.html TREATMENT FUNCTION CODE]
|
|-
|PV1:14 - Admit source
|PV1:14.1
|IS
|O
|N
|If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from [https://datadictionary.nhs.uk/attributes/source_of_admission.html SOURCE OF ADMISSION]
|
|-
|PV1:18 - Patient Type
|PV1:18.1
|IS
|R
|N
|If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from table [[Publisher API Code Sets#HL7v3-EncounterType|HL7v3-EncounterType]]
|
|-
|PV1:19 - Visit Number
|PV1:19.1
|CX
|R
|N
|Visit ID. Should uniquely identify an Encounter from the DDS publisher.
|
|-
|PV1:36 - Discharge Disposition
|PV1:36.1
|IS
|O
|N
|If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from [https://datadictionary.nhs.uk/attributes/discharge_method.html DISCHARGE METHOD]
|
|-
|PV1:37 - Discharged to Location
|PV1:37.1
|ID
|O
|N
|If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from [https://datadictionary.nhs.uk/attributes/discharge_destination.html DISCHARGE DESTINATION]
|
|-
|PV1:44 - Admit Date/Time
|PV1:44.1
|TS
|C
|N
|Admit timestamp
|
|-
|PV1:45 - Discharge Date/Time
|PV1:45.1
|TS
|O
|N
|Discharge timestamp
|
|}

== NTE - Notes and Comments ==

=== Overview ===
The NTE segment is used to hold comments [TODO - are there any constraints on the size of an individual comment]

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|NTE:3 - Comment
|NTE:3.1
|FT
|R
|Y
|Comment
|
|}

== ORC - Common Order ==

=== Overview ===
The ORC segment is used to carry information that is common across an order

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|ORC:3 - Filler Order number
|ORC:3.1
|ST
|O
|N
|This string must uniquely identify an order from other orders in the filling system
|
|-
| rowspan="4" |ORC:21 - Ordering Facility name
| -
| -
|R
|N
|Used to differentiate between different sources of result data (eg GP vs acute settings)
|
|-
|ORC:21.1
|ST
|O
|N
|Facility name
|
|-
|ORC:21.3
|ST
|R
|N
|Facility ID. Note that this MUST be the ODS code of the ordering facility
|
|-
|ORC:21.7
|ID
|R
|N
|Facility ID type. Values SHOULD be from table [[Publisher API Code Sets#0074-DiagnosticServiceSectionID|0074-DiagnosticServiceSectionID]]
|
|}

== OBR - Observation Request ==

=== Overview ===
DDS interprets an OBR as either representing a single textual laboratory report or a collection of individual test results. More information can be found in the [[Publisher API HL7 Messages#ORU_R01:_Unsolicited_Observation_.28Laboratory.29|ORU R01]] - Unsolicited Observation definition.
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|OBR:3 - Filler Order number
|OBR:3.1
|ST
|C
|N
|OBR:3-filler order number is identical to ORC:3-filler order number.
If the filler order number is not present in the ORC, it must be present in the associated OBR
|
|-
| rowspan="5" |OBR:4 - Universal Service Identifier
| -
| -
|R
|N
|
|
|-
|OBR:4.1
|ST
|R
|N
|Service Identifier
|
|-
|OBR:4.2
|ST
|R
|N
|Service name
|
|-
|OBR:4.3
|ID
|R
|N
|Service coding system. Allowed values are [TODO]
|
|-
|OBR:4.5
|ST
|O
|N
|Alternative service name
|
|-
|OBR:7 - Observation date/time
|ORC:7.1
|TS
|C
|N
|Observation timestamp. If this is not present, OBX:14 is used instead. It is an error for neither to be provided.
|
|-
| rowspan="5" |OBR:16 - Ordered by
|
|
|O
|N
|If provided, at least the family name must be given.
|
|-
|OBR:16.2
|ST
|R
|N
|Family Name
|
|-
|OBR:16.3
|ST
|O
|N
|Given Name
|
|-
|OBR:16.4
|ST
|O
|N
|Middle Names
|
|-
|OBR:16.6
|ST
|O
|N
|Prefix
|
|-
|OBR:24 - Discipline
|OBR:24.1
|ID
|O
|N
|This field is the section of the diagnostic service where the observation was performed.
If the study was performed by an outside service, the identification of that service should be recorded here. Values SHOULD be from table [[Publisher API Code Sets#0074-DiagnosticServiceSectionID|0074-DiagnosticServiceSectionID]]
|
|-
|OBR:25 - Result status
|OBR:25.1
|ID
|O
|N
|Only values of F and C will be processed. Values of I, O, P or X (which indicate pending or no results) are silently ignored, whilst any other values will cause an error. Values SHOULD be from table [[Publisher API Code Sets#DDS-ResultStatus|DDS-ResultStatus]]
|
|}

=== Definition ===

== OBX - Observation or result ==

=== Overview ===
The OBX segment can carry an observation about the patient or it can also be used to carry test results.

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|OBX:1 - Set ID
|OBX:1.1
|SI
|R
|N
|Sequence number for OBX. Must be unique under its associated OBR segment
|
|-
|OBX:2 - Value Type
|OBX:2.1
|ID
|R
|N
|Values SHOULD be from table [[Publisher API Code Sets#0125-ValueType|0125-ValueType]]
|
|-
| rowspan="4" |OBX:3 - Observation Identifier
| -
| CWE
|R
|Y
|If this is a textual report, this value will be overridden by OBR:4. Otherwise this value must be provided.
|
|-
|OBX:3.1
|ST
|R
|N
|Test ID. If this is a textual report, this value will be overridden by OBR:4.1. Otherwise this value must be provided. If a measurement is being provided, this ID must match one of the predefined values which DDS can accept. At the time of writing the acceptable code systems are still being defined though there is an aspiration to adopt the [https://digital.nhs.uk/about-nhs-digital/our-work/nhs-digital-data-and-technology-standards/clinical-information-standards#unified-test-list National Unified Test List] where appropriate. Note that this does not exclude the adoption of more appropriate code systems depending upon the nature of the observation
|
|-
|OBX:3.2
|ST
|O
|N
|Test Name
|
|-
|OBX:3.3
|ST
|C
|N
|Test coding system. If this is a textual report, this value will be overridden by OBR:4.3.
|
|-
|OBX:5 - Observation Value
| OBX:5.1
| VARIES
|R
|N
|Value. Type MUST match OBX:2.1
|
|-
| rowspan="4" |OBX:6 - Observation Units
| -
| -
|O
|N
|
|
|-
|OBX:6.1
|ST
|R
|N
|Unit ID
|
|-
|OBX:6.2
|ST
|O
|N
|Unit Name
|
|-
|OBX:6.3
|ST
|R
|N
|Unit coding system
|
|-
|OBX:7
|OBX:7.1
|ST
|O
|N
|Reference range. Can be used to convey low value, high value and normal value. DDS will parse the data into one or more of these categories depending upon how the reference range value is formatted 

* [low value]"-"[high value]
* "<"[Number] (i.e. "less than number")
* ">"[Number] (i.e. "greater than number")
* "<="[Number] (i.e. "less than or equal to number")
* ">="[Number] (i.e. "greater than or equal to number")
* [Alphabetic value indicating normal value]
|See table below for examples
|-
| rowspan="4" |OBX:8 - Abnormal flag
| -
| -
|C
|N
|To flag the result as abnormal (e.g., High, Low) this flag MUST be sent in the message
|
|-
|OBX:8.1
|
|R
|N
|Flag ID - Values SHOULD be from table <nowiki>http://terminology.hl7.org/ValueSet/v3-ObservationInterpretation</nowiki>
|
|-
|OBX:8.2
|
|O
|
|Flag name
|
|-
|OBX:8.3
|
|O
|N
|Flag coding system - If present then SHOULD be "<nowiki>http://terminology.hl7.org/ValueSet/v3-ObservationInterpretation</nowiki>"
|
|-
|OBX:11
|ST
|O
|N
|Result status
|Result status. Only values of F and C will be processed. Values of I, O, P or X (which indicate pending or no results) are silently ignored, whilst any other values will cause an error. Values SHOULD be from table [[Publisher API Code Sets#DDS-ResultStatus|DDS-ResultStatus]]
|
|-
|OBX:13
|ST
|O
|N
|Custom access rules
|Custom access rules for lab results. Currently supported: "{patientDelay:NUMBERdays}" with any whole number in place of "NUMBER" (no spaces). The patient will see that a lab result has arrived, but the result value will not be revealed until the set number of days past the date/time of observation have passed. If this field is not specified the patient will be able to see their results immediately. Note: if this OBR group is a textual report spanning multiple OBX segments, the delay must be provided in the first.
|{patientDelay:3days}
|-
|OBX:14
|ST
|C
|N
|Date/Time of the Observation
|Observation timestamp. If this is not present, OBR:7 is used instead. It is an error for neither to be provided.
|
|
|-
| rowspan="5" |OBX:16 - Responsible Observer
|
|
|O
|N
|If provided, at least the family name must be given.
|
|-
|OBX:16.2
|ST
|R
|N
|Family Name
|
|-
|OBX:16.3
|ST
|O
|N
|Given Name
|
|-
|OBX:16.4
|ST
|O
|N
|Middle Names
|
|-
|OBX:16.6
|ST
|O
|N
|Prefix
|}

=== Reference range ===
{| class="wikitable"
!OBX:7 value
!Low value
!High value
!Normal value
|-
|"3-4"
|3
|4
| -
|-
|"3--4"
|3
| -4
| -
|-
|"-3-4"
| -3
|4
| -
|-
|"3 - 4"
|3
|4
| -
|-
|" 5"
| -
| -
|5
|-
|">5"
| -
| -
|">5"
|-
|"POS"
| -
| -
|"POS"
|}

Glossary

2022-07-05T11:01:59Z

SimonMeredith:

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary. Please let us know if you would like to challenge or add definitions. simon.meredith@nhs.net or jack.barker@nhs.net
{| class="wikitable sortable"
|+
!Term
!Definition
!External Links
|-
|Canonical Data Model
|This is a type of data model that presents data entities and relationships in the simplest possible form. It is a design pattern which allows communication between different data formats. ''Canonical'' form describes the simplest representation of an object.
|[[wikipedia:Canonical_model|Canonical model - Wikipedia]]
|-
|Categories
|Broad sections of an Ontology or Classification such as the Categories in ICD 10 which contain all ICD 10 codes related to one body part. It might be also be large groupings of test results e.g. Haematology, Biochemistry, Immunology, Microbiology etc.
|[https://icd.who.int/browse10/2010/en#/II ICD 10]
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|[[wikipedia:Cloud_computing|Cloud Computing]]
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|[[wikipedia:Cloud_native_computing|Cloud Native Computing]]
|-
|Cluster
|A group of concept codes making another idea. The Cluster may be made up of concepts from different ontologies or classifications e.g., Snomed CT and ICD10. Synonyms for Cluster are refset, value set, codelist, code set, grouper. The name "cluster" was strongly advocated for by John Robson ([https://www.qmul.ac.uk/blizard/ceg/ Clinical Effectiveness Group] (CEG) NEL) because of his work in primary care.
|[http://app.powerbi.com/view?r=eyJrIjoiZTY0ODY0YzEtMjhhYy00ZTViLWJjZDQtMWZjOWVkMTZlOGExIiwidCI6IjUwZjYwNzFmLWJiZmUtNDAxYS04ODAzLTY3Mzc0OGU2MjllMiIsImMiOjh9 Primary Care Domain Reference Set Portal]
|-
|Common Data Model
|This is the shared Data Model which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
It defines the transformation of data from multiple different databases with potentially multiple coding systems into a common storage format (data model) as well as a common representation (terminologies, vocabularies, coding schemes)
|[https://www.jamesserra.com/archive/2019/06/common-data-model/ Microsoft Example]
|-
|Common Data Model (Discovery)
|The Discovery Common Data Model is the schema of the Discovery Compass v2 databases which exist in the Discovery Core and are held as subscriber databases within ICS environments. The schema is [https://wiki.discoverydataservice.org/index.php?title=Remote_Subscriber_Database_(RSD)_Schema_(Compass_2) here]..... The schema is thought to be based on EMIS primary care system schema.
|
|-
|Compass Database (Compass v2)
|The database schema used in subscriber databases in DDS. This is often referred to as the DDS Common Data Model
|[[Compass 2 Schema Mappings]]
[[Compass Database Mappings]]
|-
|Concept
|A coded idea. For example, Name = Baker's Asthma, SNOMED CT code = 34015007
|
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|
|-
|Data Dictionary
|A centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format e.g. NHS Data Dictionary.
|[[wikipedia:Data_dictionary|Data Dictionary]]
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|[[wikipedia:Data_model|Data Model]]
[https://digital.nhs.uk/services/nhs-data-model-and-dictionary-service NHS Data Model and Dictionary Service]
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|
|-
|[https://ico.org.uk/for-organisations/guide-to-data-protection/ico-codes-of-practice/data-sharing-a-code-of-practice/data-sharing-agreements/ Data Sharing Agreement] (DSA) Framework
|The arrangements for storing and managing data sharing agreements to ensure transparency across controllers and processors. It is likely that the Data Usage Committees/Data Access Groups ([https://ico.org.uk/for-organisations/guide-to-data-protection/key-dp-themes/guidance-for-the-use-of-personal-data-in-political-campaigning-1/controllers-joint-controllers-and-processors/ Joint Controllers]) will generate and store a copy of the DSA, whilst processors and sub-processors will also need a copy.
|
|-
|Data Warehouse
|A data warehouse stores data for the main purpose of reporting and data analysis. They are central repositories which bring together disparate data sets into one place, to enable reporting at scale and pace.
|[[wikipedia:Data_warehouse|Data Warehouse]]
|-
|Dataset
|A '''data set''' (or '''dataset''') is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question. The data set lists values for each of the variables, such as for example height and weight of an object, for each member of the data set. Data sets can also consist of a collection of documents or files.

In the Discovery Data Service datasets may be generated as a result of queries and valueset filters on the internal relational database.
|[[wikipedia:Data_set|Dataset]]
|-
|Discovery Collaborative
|A collaboration of three integrated care systems (ICS) with the ambition to develop and maintain a data service. It arose out of NE London where GPs and academics were working with a charitable trust to develop a secure, trusted, aggregated information resource to support quality improvement and research. As part of the national Local Health and Care Record Exemplar programme it expanded to NW and SE London ICSs. The Collaborative has a Board and working groups. The Chair of the Board reports to the Senior Responsible Officer for digital in each of the three ICSs.
|
|-
|Discovery Data Service (DDS)
|The hardware and software made available by the Discovery Collaborative. The DDS is not an organisation. The management and development of the bulk of the DDS is currently sub-contracted to a private company Voror in an Amazon Web Service Environment. However, there have been discussions about in-housing the service under closer control by NEL CCG and moving the service to a Microsoft Azure environment.
|
|-
|Discovery Health Information Model
|The "toolkit " of the Discovery Data Service. 1) A set of ontologies and classifications 2) A common data model 3) A metadata library of clusters of codes (codesets, valuesets etc) - currently the IM1 data tables in the DDS that are managed through IM2 (see below) 4) A catalogue of reference data (such as geographical areas, organisations and people). 5) A library of queries 6) A set of “maps” (mappings) 7) An Information Manager - currently (Endeavour Information Manager 2 - IM2) - an open source set of utilities that can be used to browse, search, or maintain the model.
|[https://wiki.endeavourhealth.org/index.php?title=Discovery_health_information_model Discovery Health Information Model]
|-
|Endeavour Information (Model) Manager
|The Information Model Manager is an application developed by the Endeavour Charitable Trust which provides a view of the Common Information Model and supports authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|[https://im.endeavourhealth.net/#/ Endeavour IM2]
|-
|Environment (computing)
|An environment is the collection of computer machinery, data storage devices, work stations, software applications, and networks that support the processing and exchange of electronic information required by the software application. Typically there will be several environments for one system, often including a development environment (where developers can test out new code and see how it interacts), a staging, or test, environment (where new code is subject to an automated test suite comprising end-to-end tests, regression testing, unit and integration tests, as well as manual User Acceptance Testing), and a production environment (which is where the live application runs).
|
|-
|Expression (Snomed CT)
|A combination of concepts that represent something that is not already represented as a Concept or Cluster. It typically links things across concepts (disease of infection (finding), with bacteria name (organism), with sepsis (observable entity) with kidney (body structure).
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/SNOMED+CT+expression Snomed CT Expression]
|-
|Expression Constraint Language
|The ''Expression Constraint Language'' is a formal syntax for representing SNOMED CT expression constraints. Expression constraints are computable rules used to define a bounded sets of clinical meanings represented by either precoordinated or postcoordinated expressions. Expression constraints can be used to restrict the valid values for a data element in an EHR, as the intensional definition of a concept-based reference set, as a machine processable query that identifies a set of matching expressions, or as a constraint that restricts the range of an attribute defined in the SNOMED CT concept model.
|Expression Constraint [https://confluence.ihtsdotools.org/display/DOCECL Language]
|-
|Facts
|Patient "Facts" - A concept introduced by NEL CSU to include '''1) Clinical components''' - effectively cohorts or registers - for instance long term conditions with national (e.g. QOF) or locally defined definitions e.g. Hypertension, Asthma '''2) Segmentation models''' e.g. "frailty" or likelihood of admission. '''3) Care quality standard compliance''' - quality measures which identify whether care standards are being met i.e. has a medication review or health check been done? Has the patient had each of 8 care processes for diabetes? Has the patient on a hypertension register had a blood pressure scheck and met the target?
Developing these at a London scale might be an excellent way to promote standardised Population Health Management.
|
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|
|-
|Functional Requirements
|These are product features or functionality that must be developed in order for the system to satisfy requirements. They must be implemented in the system in order for it to achieve what it is supposed to accomplish. Examples include "users should be able to download data based on their filtered search criteria" or "only admin-level users should be able to reset passwords for other users"
|[[wikipedia:Functional_requirement|Functional requirement]]
|-
|GitHub
|GitHub is a provider of version control and source-code management using [[wikipedia:Git|Git]] (which is software used for tracking changes in files). It enables software developers to safely store source code and work on it collaboratively, merging changes from different developers into the main "production" branch safely. It provides additional features such as bug/issue tracking, continuous integration, feature requests etc. GitHub was acquired by Microsoft in 2018.
|[[wikipedia:GitHub|GitHub]]
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|
|-
|HDRUK Phenotype Library
|This is a comprehensive, open access resource which exists to provide the research community with information, tools, and phenotyping algorithms for UK electronic health records (EHRs)
|[https://phenotypes.healthdatagateway.org/ HDR UK Phenotype Library]
|-
|Information Model
|A model describing ontologically standardised data items following a specific database schema within a specific relational database architecture.
It is an abstract model, showing the constraints, relationships, concepts, and data items for a domain.

The Discovery Information Model is a representation of the meaning and structure of data held in the electronic records of the health and social care sector. It includes libraries of queries, value sets, concept sets, data set definitions and mappings. The main purpose is to bridge the chasm that exists between highly technical digital representations and plain language so that when questions are asked of data, a lay person could use plain language without prior knowledge of the underlying models.
|[[wikipedia:Information_model|Information Model]]

[[Discovery health information model|DDS IM]]

[https://im.endeavourhealth.net/#/ IM Viewer]

[https://blog.softwaresuperglue.com/2018/11/09/information-model-vs-data-model/ Information Model vs Data Model]
|-
|Integrated Care Board (ICB)
|The ICB is a legal entity created in the Health and Care Act 2022. ICBs will bring the NHS together locally to deliver shared priorities, with a greater emphasis on collaboration and shared responsibility for the health of the local population. This will require governance arrangements that support collective accountability between partner organisations for whole-system delivery and performance. These arrangements should be proportionate, and they must facilitate transparent decision-making and foster the culture and behaviours that enable system working.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
[https://www.legislation.gov.uk/ukpga/2022/31/contents/enacted Health and Care Act 2022]
|-
|Integrated Care Partnership (ICP)
|The ICP is a joint committee of the ICB and the upper tier local authorities that are wholly or partly in the ICB area. From 1 July, the ICB and the local authorities will be under a legal duty to establish the ICP.
It is the role of the ICP to develop and publish the integrated care strategy for the ICB area, in particular focusing on how health and care can better integrate.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
|-
|Integrated Care System (ICS)
|Integrated care systems (ICSs) are partnerships of health and care organisations that come together to plan and deliver joined up services and to improve the health of people who live and work in their area.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
|-
|Joint Controller Agreement (JCA)
|The arrangements by which a group of data controllers agree to work together to agree the uses of data and how they jointly take responsibility for that. This is frequently manifested as the development of a representative committee that reviews the five safes, the legal basis for use of personal data, the worthiness of the proposed use of data
|[https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/controllers-and-processors/what-does-it-mean-if-you-are-joint-controllers/ Joint Controllers]
|-
|Knowledge Graph
|An information architecture where concepts are represented as nodes and edges in a network of relationships. This is usually represented in a NoSQL architecture designed to around relationships between concepts.
|
|-
|[[Logical Data Model]]
|The logical data model (or logical schema) describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|[[wikipedia:Logical_schema|Logical Schema]]
|-
|London Care Record
|A shared care record used for individual care. It is based on Cerner Health Information Exchange architecture.
|
|-
|Mauro Data Mapper
|Mauro Data Mapper is a third-party toolkit for the design and documentation of databases, data flows, and data standards, as well as related software artefacts such as data schemas and data forms. It was originally developed for the description of data in clinical research.
|[https://maurodatamapper.github.io/about/introduction/ Mauro Data Mapper]
|-
|Medical Classification
|Medical Classifications transform descriptions of procedures or diagnoses into standardised codes through the process of clinical coding. E.g., ICD10, OPCS4, LOINC. These
can also be called code sets. Hence, we call them a medical classification to avoid confusion with Clusters
|[[wikipedia:Medical_classification|Medical Classification]]
|-
|Metadata Library
|A tool that allows management of clusters
|
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|[https://digital.nhs.uk/services/terminology-servers NHSD Terminology Server]
|-
|Non-functional Requirements
|These are requirements concerning the operation of a system rather than functionality, and typically relate to security, scalability, performance, reliability etc. Examples include "the page must load within 500ms" or "the website must conform to WCAG AA accessibility standards"
|[[wikipedia:Non-functional_requirement|Non-functional Requirements]]
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|[https://www.imohealth.com/ideas/article/data-normalization-working-toward-a-common-clinical-terminology/ Healthcare Normalisation]
[https://blog.clinicalarchitecture.com/what-is-data-normalization What is data normalisation?]
|-
|Normalisation (database)
|This is the process of structuring a relational database in accordance with a series of derived rules, called ''normal forms'', in order to reduce redundancy, duplication, and improve data integrity.
|[[wikipedia:Database_normalization|Normalisation]]
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|
|-
|OMOP Data Model
|OMOP stands for Observational Medical Outcomes Partnership, which was formed to inform the appropriate use of observational healthcare databases. OHDSI (Observational Health Data Sciences and Informatics) is a collaborative that now includes all of the original OMOP research investigators and will continue to develop tools using the OMOP common data model and vocabulary (OMOP is no longer and active programme). OMOP is cited in the UK Health Data Research paper on building Building (federated )[https://zenodo.org/record/5767586/files/211208%20Building%20TREs%20Paper%20v1.0.pdf Trusted Research Environments] (p22).
|[https://www.ohdsi.org/data-standardization/the-common-data-model/ OMOP]
[https://github.com/OHDSI/CommonDataModel/blob/v5.4.0/inst/ddl/5.4/sql_server/OMOPCDM_sql_server_5.4_ddl.sql OMOP Common Data Model (GitHub)]
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. The most currently used ontology is SNOMED CT - the DDS ontology is made up of several ontologies (such as READ, CTV3 etc) mapped to SNOMED where appropriate SNOMED terms already exist, and extended where they don't.
A representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse (e.g., medicine). Example: Snomed CT.
|[[wikipedia:Ontology_(information_science)|Ontology]]
[https://termbrowser.nhs.uk/ NHSD SNOMED browser]

[https://www.snomed.org/ SNOMED.org]

[https://www.england.nhs.uk/digitaltechnology/digital-primary-care/snomed-ct/ SNOMED - NHS England]
|-
|OpenCodelists
|OpenCodelists was created by OpenSAFELY for creating and sharing codelists. See "Cluster" definition above.
|[https://www.opencodelists.org/ OpenCodelists]
[https://www.opensafely.org/ OpenSAFELY]
|-
|PESTLE Analysis
|A PESTLE analysis studies the key external factors (Political, Economic, Sociological, Technological, Legal and Environmental) that influence an organisation. It can be used in a range of different scenarios, and can guide people professionals and senior managers in strategic decision-making
|[https://www.cipd.co.uk/knowledge/strategy/organisational-development/pestle-analysis-factsheet Pestle Analysis]
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|[[wikipedia:Physical_schema|Physical Schema]]
|-
|Primary Care Reference Set
|This is a cluster of codes used within business rules authored and maintained by NHSD's primary care domain.
|[https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/quality-and-outcomes-framework-qof/quality-and-outcome-framework-qof-business-rules/primary-care-domain-reference-set-portal NHSD Primary Care Domain Reference Set Portal]
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SNOMED CT Concept
|A clinical idea to which a unique concept identifier has been assigned.
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/concept SNOMED CT Concept]
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|[[wikipedia:SQL|Structured Query Language]]
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|
|-
|Star Schema
|A simple database schema which consists of fact tables and dimension tables
|[[wikipedia:Star_schema|Star schema]]
|-
|Subscription (Azure)
|An Azure Subscription is linked to a payment setup, and each subscription results in a separate bill. Several subscriptions can be owned by one tenant. Under each subscription, you can add resources, such as virtual machines, storage, networks etc.
|[https://www.parallels.com/blogs/ras/azure-subscription/#:~:text=An%20Azure%20subscription%20is%20a,are%20used%20and%20billed%20together. Azure Subscription] 
|-
|Tenancy/Tenant (Azure)
|An Azure tenant represents an organisation. It is a dedicated instance of Azure Active Directory (Azure AD - which is an enterprise identity service which holds all the user accounts, and provides role-based access, single sign-on, multifactor authentication etc ensuring only the right people have access to the right resources, only when they need it). Each Azure tenant is distinct and separate from other Azure AD tenants. It should not be confused with an Azure Subscription
|[https://docs.microsoft.com/en-us/azure/active-directory/fundamentals/active-directory-whatis Azure Active Directory] 
|-
|Trusted Data Environment
|Raw [health and care] data is not powerful on its own. It must be shaped, checked, and curated into shape. It must be housed, and managed securely. It must be analysed. And then it must be communicated, and acted upon. That work all requires people, with modern data skills, in teams, using platforms that protect patients’ privacy and avoid needless duplication of effort."
|[https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1067053/goldacre-review-using-health-data-for-research-and-analysis.pdf Better, Broader, Safer: Using Health Data for Research and Analysis]
|-
|Trusted Research Environment
|An approach to data access based primarily around Trusted (Trustworthy) Research Environments (TREs), a type of Secure Data Environment; with
appropriate robust and independent accreditation, monitoring and auditing.
|[https://zenodo.org/record/5767586/files/211208%20Building%20TREs%20Paper%20v1.0.pdf Building Trusted Research Environments. Principles and Best Practices; towards TRE ecosystems.]
|-
|Value Set
|A generic term for a set of values, or codes, which might be stored in a metadata library or other set of lookup data. Value sets are used as the filters of data to create Data Sets - for example, in the case of populating the London Care Record from DDS, Value Sets are used to filter the underlying data in order to create a Data Set which is suitable for ingestion.
Concepts and Clusters are types of Value Set.

Code Set is sometimes used interchangeably with Value Set
|
|}

Glossary

2022-07-04T09:41:55Z

SimonMeredith: added tenant, environment, subscription

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary. Please let us know if you would like to challenge or add definitions. simon.meredith@nhs.net or jack.barker@nhs.net
{| class="wikitable sortable"
|+
!Term
!Definition
!External Links
|-
|Canonical Data Model
|This is a type of data model that presents data entities and relationships in the simplest possible form. It is a design pattern which allows communication between different data formats. ''Canonical'' form describes the simplest representation of an object.
|[[wikipedia:Canonical_model|Canonical model - Wikipedia]]
|-
|Categories
|Broad sections of an Ontology or Classification such as the Categories in ICD 10 which contain all ICD 10 codes related to one body part. It might be also be large groupings of test results e.g. Haematology, Biochemistry, Immunology, Microbiology etc.
|[https://icd.who.int/browse10/2010/en#/II ICD 10]
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|[[wikipedia:Cloud_computing|Cloud Computing]]
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|[[wikipedia:Cloud_native_computing|Cloud Native Computing]]
|-
|Cluster
|A group of concept codes making another idea. The Cluster may be made up of concepts from different ontologies or classifications e.g., Snomed CT and ICD10. Synonyms for Cluster are refset, value set, codelist, code set, grouper. The name "cluster" was strongly advocated for by John Robson ([https://www.qmul.ac.uk/blizard/ceg/ Clinical Effectiveness Group] (CEG) NEL) because of his work in primary care.
|[http://app.powerbi.com/view?r=eyJrIjoiZTY0ODY0YzEtMjhhYy00ZTViLWJjZDQtMWZjOWVkMTZlOGExIiwidCI6IjUwZjYwNzFmLWJiZmUtNDAxYS04ODAzLTY3Mzc0OGU2MjllMiIsImMiOjh9 Primary Care Domain Reference Set Portal]
|-
|Common Data Model
|This is the shared Data Model which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
It defines the transformation of data from multiple different databases with potentially multiple coding systems into a common storage format (data model) as well as a common representation (terminologies, vocabularies, coding schemes)
|[https://www.jamesserra.com/archive/2019/06/common-data-model/ Microsoft Example]
|-
|Common Data Model (Discovery)
|The Discovery Common Data Model is the schema of the Discovery Compass v2 databases which exist in the Discovery Core and are held as subscriber databases within ICS environments. The schema is [https://wiki.discoverydataservice.org/index.php?title=Remote_Subscriber_Database_(RSD)_Schema_(Compass_2) here]..... The schema is thought to be based on EMIS primary care system schema.
|
|-
|Concept
|A coded idea. For example, Name = Baker's Asthma, SNOMED CT code = 34015007
|
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|
|-
|Data Dictionary
|A centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format e.g. NHS Data Dictionary.
|[[wikipedia:Data_dictionary|Data Dictionary]]
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|[[wikipedia:Data_model|Data Model]]
[https://digital.nhs.uk/services/nhs-data-model-and-dictionary-service NHS Data Model and Dictionary Service]
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|
|-
|[https://ico.org.uk/for-organisations/guide-to-data-protection/ico-codes-of-practice/data-sharing-a-code-of-practice/data-sharing-agreements/ Data Sharing Agreement] (DSA) Framework
|The arrangements for storing and managing data sharing agreements to ensure transparency across controllers and processors. It is likely that the Data Usage Committees/Data Access Groups ([https://ico.org.uk/for-organisations/guide-to-data-protection/key-dp-themes/guidance-for-the-use-of-personal-data-in-political-campaigning-1/controllers-joint-controllers-and-processors/ Joint Controllers]) will generate and store a copy of the DSA, whilst processors and sub-processors will also need a copy.
|
|-
|Data Warehouse
|A data warehouse stores data for the main purpose of reporting and data analysis. They are central repositories which bring together disparate data sets into one place, to enable reporting at scale and pace.
|[[wikipedia:Data_warehouse|Data Warehouse]]
|-
|Dataset
|A '''data set''' (or '''dataset''') is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question. The data set lists values for each of the variables, such as for example height and weight of an object, for each member of the data set. Data sets can also consist of a collection of documents or files.

In the Discovery Data Service datasets may be generated as a result of queries and valueset filters on the internal relational database.
|[[wikipedia:Data_set|Dataset]]
|-
|Discovery Collaborative
|A collaboration of three integrated care systems (ICS) with the ambition to develop and maintain a data service. It arose out of NE London where GPs and academics were working with a charitable trust to develop a secure, trusted, aggregated information resource to support quality improvement and research. As part of the national Local Health and Care Record Exemplar programme it expanded to NW and SE London ICSs. The Collaborative has a Board and working groups. The Chair of the Board reports to the Senior Responsible Officer for digital in each of the three ICSs.
|
|-
|Discovery Data Service (DDS)
|The hardware and software made available by the Discovery Collaborative. The DDS is not an organisation. The management and development of the bulk of the DDS is currently sub-contracted to a private company Voror in an Amazon Web Service Environment. However, there have been discussions about in-housing the service under closer control by NEL CCG and moving the service to a Microsoft Azure environment.
|
|-
|Discovery Health Information Model
|The "toolkit " of the Discovery Data Service. 1) A set of ontologies and classifications 2) A common data model 3) A metadata library of clusters of codes (codesets, valuesets etc) - currently the IM1 data tables in the DDS that are managed through IM2 (see below) 4) A catalogue of reference data (such as geographical areas, organisations and people). 5) A library of queries 6) A set of “maps” (mappings) 7) An Information Manager - currently (Endeavour Information Manager 2 - IM2) - an open source set of utilities that can be used to browse, search, or maintain the model.
|[https://wiki.endeavourhealth.org/index.php?title=Discovery_health_information_model Discovery Health Information Model]
|-
|Endeavour Information (Model) Manager
|The Information Model Manager is an application developed by the Endeavour Charitable Trust which provides a view of the Common Information Model and supports authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|[https://im.endeavourhealth.net/#/ Endeavour IM2]
|-
|Environment (computing)
|An environment is the collection of computer machinery, data storage devices, work stations, software applications, and networks that support the processing and exchange of electronic information required by the software application. Typically there will be several environments for one system, often including a development environment (where developers can test out new code and see how it interacts), a staging, or test, environment (where new code is subject to an automated test suite comprising end-to-end tests, regression testing, unit and integration tests, as well as manual User Acceptance Testing), and a production environment (which is where the live application runs).
|
|-
|Expression (Snomed CT)
|A combination of concepts that represent something that is not already represented as a Concept or Cluster. It typically links things across concepts (disease of infection (finding), with bacteria name (organism), with sepsis (observable entity) with kidney (body structure).
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/SNOMED+CT+expression Snomed CT Expression]
|-
|Expression Constraint Language
|The ''Expression Constraint Language'' is a formal syntax for representing SNOMED CT expression constraints. Expression constraints are computable rules used to define a bounded sets of clinical meanings represented by either precoordinated or postcoordinated expressions. Expression constraints can be used to restrict the valid values for a data element in an EHR, as the intensional definition of a concept-based reference set, as a machine processable query that identifies a set of matching expressions, or as a constraint that restricts the range of an attribute defined in the SNOMED CT concept model.
|Expression Constraint [https://confluence.ihtsdotools.org/display/DOCECL Language]
|-
|Facts
|Patient "Facts" - A concept introduced by NEL CSU to include '''1) Clinical components''' - effectively cohorts or registers - for instance long term conditions with national (e.g. QOF) or locally defined definitions e.g. Hypertension, Asthma '''2) Segmentation models''' e.g. "frailty" or likelihood of admission. '''3) Care quality standard compliance''' - quality measures which identify whether care standards are being met i.e. has a medication review or health check been done? Has the patient had each of 8 care processes for diabetes? Has the patient on a hypertension register had a blood pressure scheck and met the target?
Developing these at a London scale might be an excellent way to promote standardised Population Health Management.
|
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|
|-
|Functional Requirements
|These are product features or functionality that must be developed in order for the system to satisfy requirements. They must be implemented in the system in order for it to achieve what it is supposed to accomplish. Examples include "users should be able to download data based on their filtered search criteria" or "only admin-level users should be able to reset passwords for other users"
|[[wikipedia:Functional_requirement|Functional requirement]]
|-
|GitHub
|GitHub is a provider of version control and source-code management using [[wikipedia:Git|Git]] (which is software used for tracking changes in files). It enables software developers to safely store source code and work on it collaboratively, merging changes from different developers into the main "production" branch safely. It provides additional features such as bug/issue tracking, continuous integration, feature requests etc. GitHub was acquired by Microsoft in 2018.
|[[wikipedia:GitHub|GitHub]]
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|
|-
|HDRUK Phenotype Library
|This is a comprehensive, open access resource which exists to provide the research community with information, tools, and phenotyping algorithms for UK electronic health records (EHRs)
|[https://phenotypes.healthdatagateway.org/ HDR UK Phenotype Library]
|-
|Information Model
|A model describing ontologically standardised data items following a specific database schema within a specific relational database architecture.
It is an abstract model, showing the constraints, relationships, concepts, and data items for a domain.

The Discovery Information Model is a representation of the meaning and structure of data held in the electronic records of the health and social care sector. It includes libraries of queries, value sets, concept sets, data set definitions and mappings. The main purpose is to bridge the chasm that exists between highly technical digital representations and plain language so that when questions are asked of data, a lay person could use plain language without prior knowledge of the underlying models.
|[[wikipedia:Information_model|Information Model]]

[[Discovery health information model|DDS IM]]

[https://im.endeavourhealth.net/#/ IM Viewer]

[https://blog.softwaresuperglue.com/2018/11/09/information-model-vs-data-model/ Information Model vs Data Model]
|-
|Integrated Care Board (ICB)
|The ICB is a legal entity created in the Health and Care Act 2022. ICBs will bring the NHS together locally to deliver shared priorities, with a greater emphasis on collaboration and shared responsibility for the health of the local population. This will require governance arrangements that support collective accountability between partner organisations for whole-system delivery and performance. These arrangements should be proportionate, and they must facilitate transparent decision-making and foster the culture and behaviours that enable system working.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
[https://www.legislation.gov.uk/ukpga/2022/31/contents/enacted Health and Care Act 2022]
|-
|Integrated Care Partnership (ICP)
|The ICP is a joint committee of the ICB and the upper tier local authorities that are wholly or partly in the ICB area. From 1 July, the ICB and the local authorities will be under a legal duty to establish the ICP.
It is the role of the ICP to develop and publish the integrated care strategy for the ICB area, in particular focusing on how health and care can better integrate.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
|-
|Integrated Care System (ICS)
|Integrated care systems (ICSs) are partnerships of health and care organisations that come together to plan and deliver joined up services and to improve the health of people who live and work in their area.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
|-
|Joint Controller Agreement (JCA)
|The arrangements by which a group of data controllers agree to work together to agree the uses of data and how they jointly take responsibility for that. This is frequently manifested as the development of a representative committee that reviews the five safes, the legal basis for use of personal data, the worthiness of the proposed use of data
|[https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/controllers-and-processors/what-does-it-mean-if-you-are-joint-controllers/ Joint Controllers]
|-
|Knowledge Graph
|An information architecture where concepts are represented as nodes and edges in a network of relationships. This is usually represented in a NoSQL architecture designed to around relationships between concepts.
|
|-
|[[Logical Data Model]]
|The logical data model (or logical schema) describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|[[wikipedia:Logical_schema|Logical Schema]]
|-
|London Care Record
|A shared care record used for individual care. It is based on Cerner Health Information Exchange architecture.
|
|-
|Mauro Data Mapper
|Mauro Data Mapper is a third-party toolkit for the design and documentation of databases, data flows, and data standards, as well as related software artefacts such as data schemas and data forms. It was originally developed for the description of data in clinical research.
|[https://maurodatamapper.github.io/about/introduction/ Mauro Data Mapper]
|-
|Medical Classification
|Medical Classifications transform descriptions of procedures or diagnoses into standardised codes through the process of clinical coding. E.g., ICD10, OPCS4, LOINC. These
can also be called code sets. Hence, we call them a medical classification to avoid confusion with Clusters
|[[wikipedia:Medical_classification|Medical Classification]]
|-
|Metadata Library
|A tool that allows management of clusters
|
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|[https://digital.nhs.uk/services/terminology-servers NHSD Terminology Server]
|-
|Non-functional Requirements
|These are requirements concerning the operation of a system rather than functionality, and typically relate to security, scalability, performance, reliability etc. Examples include "the page must load within 500ms" or "the website must conform to WCAG AA accessibility standards"
|[[wikipedia:Non-functional_requirement|Non-functional Requirements]]
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|[https://www.imohealth.com/ideas/article/data-normalization-working-toward-a-common-clinical-terminology/ Healthcare Normalisation]
[https://blog.clinicalarchitecture.com/what-is-data-normalization What is data normalisation?]
|-
|Normalisation (database)
|This is the process of structuring a relational database in accordance with a series of derived rules, called ''normal forms'', in order to reduce redundancy, duplication, and improve data integrity.
|[[wikipedia:Database_normalization|Normalisation]]
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|
|-
|OMOP Data Model
|OMOP stands for Observational Medical Outcomes Partnership, which was formed to inform the appropriate use of observational healthcare databases. OHDSI (Observational Health Data Sciences and Informatics) is a collaborative that now includes all of the original OMOP research investigators and will continue to develop tools using the OMOP common data model and vocabulary (OMOP is no longer and active programme). OMOP is cited in the UK Health Data Research paper on building Building (federated )[https://zenodo.org/record/5767586/files/211208%20Building%20TREs%20Paper%20v1.0.pdf Trusted Research Environments] (p22).
|[https://www.ohdsi.org/data-standardization/the-common-data-model/ OMOP]
[https://github.com/OHDSI/CommonDataModel/blob/v5.4.0/inst/ddl/5.4/sql_server/OMOPCDM_sql_server_5.4_ddl.sql OMOP Common Data Model (GitHub)]
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. The most currently used ontology is SNOMED CT - the DDS ontology is made up of several ontologies (such as READ, CTV3 etc) mapped to SNOMED where appropriate SNOMED terms already exist, and extended where they don't.
A representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse (e.g., medicine). Example: Snomed CT.
|[[wikipedia:Ontology_(information_science)|Ontology]]
[https://termbrowser.nhs.uk/ NHSD SNOMED browser]

[https://www.snomed.org/ SNOMED.org]

[https://www.england.nhs.uk/digitaltechnology/digital-primary-care/snomed-ct/ SNOMED - NHS England]
|-
|OpenCodelists
|OpenCodelists was created by OpenSAFELY for creating and sharing codelists. See "Cluster" definition above.
|[https://www.opencodelists.org/ OpenCodelists]
[https://www.opensafely.org/ OpenSAFELY]
|-
|PESTLE Analysis
|A PESTLE analysis studies the key external factors (Political, Economic, Sociological, Technological, Legal and Environmental) that influence an organisation. It can be used in a range of different scenarios, and can guide people professionals and senior managers in strategic decision-making
|[https://www.cipd.co.uk/knowledge/strategy/organisational-development/pestle-analysis-factsheet Pestle Analysis]
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|[[wikipedia:Physical_schema|Physical Schema]]
|-
|Primary Care Reference Set
|This is a cluster of codes used within business rules authored and maintained by NHSD's primary care domain.
|[https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/quality-and-outcomes-framework-qof/quality-and-outcome-framework-qof-business-rules/primary-care-domain-reference-set-portal NHSD Primary Care Domain Reference Set Portal]
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SNOMED CT Concept
|A clinical idea to which a unique concept identifier has been assigned.
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/concept SNOMED CT Concept]
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|[[wikipedia:SQL|Structured Query Language]]
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|
|-
|Star Schema
|A simple database schema which consists of fact tables and dimension tables
|[[wikipedia:Star_schema|Star schema]]
|-
|Subscription (Azure)
|An Azure Subscription is linked to a payment setup, and each subscription results in a separate bill. Several subscriptions can be owned by one tenant. Under each subscription, you can add resources, such as virtual machines, storage, networks etc.
|[https://www.parallels.com/blogs/ras/azure-subscription/#:~:text=An%20Azure%20subscription%20is%20a,are%20used%20and%20billed%20together. Azure Subscription] 
|-
|Tenancy/Tenant (Azure)
|An Azure tenant represents an organisation. It is a dedicated instance of Azure Active Directory (Azure AD - which is an enterprise identity service which holds all the user accounts, and provides role-based access, single sign-on, multifactor authentication etc ensuring only the right people have access to the right resources, only when they need it). Each Azure tenant is distinct and separate from other Azure AD tenants. It should not be confused with an Azure Subscription
|[https://docs.microsoft.com/en-us/azure/active-directory/fundamentals/active-directory-whatis Azure Active Directory] 
|-
|Trusted Data Environment
|Raw [health and care] data is not powerful on its own. It must be shaped, checked, and curated into shape. It must be housed, and managed securely. It must be analysed. And then it must be communicated, and acted upon. That work all requires people, with modern data skills, in teams, using platforms that protect patients’ privacy and avoid needless duplication of effort."
|[https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1067053/goldacre-review-using-health-data-for-research-and-analysis.pdf Better, Broader, Safer: Using Health Data for Research and Analysis]
|-
|Trusted Research Environment
|An approach to data access based primarily around Trusted (Trustworthy) Research Environments (TREs), a type of Secure Data Environment; with
appropriate robust and independent accreditation, monitoring and auditing.
|[https://zenodo.org/record/5767586/files/211208%20Building%20TREs%20Paper%20v1.0.pdf Building Trusted Research Environments. Principles and Best Practices; towards TRE ecosystems.]
|-
|Value Set
|A generic term for a set of values, or codes, which might be stored in a metadata library or other set of lookup data. Value sets are used as the filters of data to create Data Sets - for example, in the case of populating the London Care Record from DDS, Value Sets are used to filter the underlying data in order to create a Data Set which is suitable for ingestion.
Concepts and Clusters are types of Value Set.

Code Set is sometimes used interchangeably with Value Set
|
|}

Glossary

2022-06-17T12:38:41Z

SimonMeredith: added star schema

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary. Please let us know if you would like to challenge or add definitions. simon.meredith@nhs.net or jack.barker@nhs.net
{| class="wikitable sortable"
|+
!Term
!Definition
!External Links
|-
|Canonical Data Model
|This is a type of data model that presents data entities and relationships in the simplest possible form. It is a design pattern which allows communication between different data formats. ''Canonical'' form describes the simplest representation of an object.
|[[wikipedia:Canonical_model|Canonical model - Wikipedia]]
|-
|Categories
|Broad sections of an Ontology or Classification such as the Categories in ICD 10 which contain all ICD 10 codes related to one body part. It might be also be large groupings of test results e.g. Haematology, Biochemistry, Immunology, Microbiology etc.
|[https://icd.who.int/browse10/2010/en#/II ICD 10]
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|[[wikipedia:Cloud_computing|Cloud Computing]]
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|[[wikipedia:Cloud_native_computing|Cloud Native Computing]]
|-
|Cluster
|A group of concept codes making another idea. The Cluster may be made up of concepts from different ontologies or classifications e.g., Snomed CT and ICD10. Synonyms for Cluster are refset, value set, codelist, code set, grouper. The name "cluster" was strongly advocated for by John Robson ([https://www.qmul.ac.uk/blizard/ceg/ Clinical Effectiveness Group] (CEG) NEL) because of his work in primary care.
|[http://app.powerbi.com/view?r=eyJrIjoiZTY0ODY0YzEtMjhhYy00ZTViLWJjZDQtMWZjOWVkMTZlOGExIiwidCI6IjUwZjYwNzFmLWJiZmUtNDAxYS04ODAzLTY3Mzc0OGU2MjllMiIsImMiOjh9 Primary Care Domain Reference Set Portal]
|-
|Common Data Model
|This is the shared Data Model which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
It defines the transformation of data from multiple different databases with potentially multiple coding systems into a common storage format (data model) as well as a common representation (terminologies, vocabularies, coding schemes)
|[https://www.jamesserra.com/archive/2019/06/common-data-model/ Microsoft Example]
|-
|Common Data Model (Discovery)
|The Discovery Common Data Model is the schema of the Discovery Compass v2 databases which exist in the Discovery Core and are held as subscriber databases within ICS environments. The schema is [https://wiki.discoverydataservice.org/index.php?title=Remote_Subscriber_Database_(RSD)_Schema_(Compass_2) here]..... The schema is thought to be based on EMIS primary care system schema.
|
|-
|Concept
|A coded idea. For example, Name = Baker's Asthma, SNOMED CT code = 34015007
|
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|
|-
|Data Dictionary
|A centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format e.g. NHS Data Dictionary.
|[[wikipedia:Data_dictionary|Data Dictionary]]
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|[[wikipedia:Data_model|Data Model]]
[https://digital.nhs.uk/services/nhs-data-model-and-dictionary-service NHS Data Model and Dictionary Service]
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|
|-
|[https://ico.org.uk/for-organisations/guide-to-data-protection/ico-codes-of-practice/data-sharing-a-code-of-practice/data-sharing-agreements/ Data Sharing Agreement] (DSA) Framework
|The arrangements for storing and managing data sharing agreements to ensure transparency across controllers and processors. It is likely that the Data Usage Committees/Data Access Groups ([https://ico.org.uk/for-organisations/guide-to-data-protection/key-dp-themes/guidance-for-the-use-of-personal-data-in-political-campaigning-1/controllers-joint-controllers-and-processors/ Joint Controllers]) will generate and store a copy of the DSA, whilst processors and sub-processors will also need a copy.
|
|-
|Data Warehouse
|A data warehouse stores data for the main purpose of reporting and data analysis. They are central repositories which bring together disparate data sets into one place, to enable reporting at scale and pace.
|[[wikipedia:Data_warehouse|Data Warehouse]]
|-
|Dataset
|A '''data set''' (or '''dataset''') is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question. The data set lists values for each of the variables, such as for example height and weight of an object, for each member of the data set. Data sets can also consist of a collection of documents or files.

In the Discovery Data Service datasets may be generated as a result of queries and valueset filters on the internal relational database.
|[[wikipedia:Data_set|Dataset]]
|-
|Discovery Collaborative
|A collaboration of three integrated care systems (ICS) with the ambition to develop and maintain a data service. It arose out of NE London where GPs and academics were working with a charitable trust to develop a secure, trusted, aggregated information resource to support quality improvement and research. As part of the national Local Health and Care Record Exemplar programme it expanded to NW and SE London ICSs. The Collaborative has a Board and working groups. The Chair of the Board reports to the Senior Responsible Officer for digital in each of the three ICSs.
|
|-
|Discovery Data Service (DDS)
|The hardware and software made available by the Discovery Collaborative. The DDS is not an organisation. The management and development of the bulk of the DDS is currently sub-contracted to a private company Voror in an Amazon Web Service Environment. However, there have been discussions about in-housing the service under closer control by NEL CCG and moving the service to a Microsoft Azure environment.
|
|-
|Discovery Health Information Model
|The "toolkit " of the Discovery Data Service. 1) A set of ontologies and classifications 2) A common data model 3) A metadata library of clusters of codes (codesets, valuesets etc) - currently the IM1 data tables in the DDS that are managed through IM2 (see below) 4) A catalogue of reference data (such as geographical areas, organisations and people). 5) A library of queries 6) A set of “maps” (mappings) 7) An Information Manager - currently (Endeavour Information Manager 2 - IM2) - an open source set of utilities that can be used to browse, search, or maintain the model.
|[https://wiki.endeavourhealth.org/index.php?title=Discovery_health_information_model Discovery Health Information Model]
|-
|Endeavour Information (Model) Manager
|The Information Model Manager is an application developed by the Endeavour Charitable Trust which provides a view of the Common Information Model and supports authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|[https://im.endeavourhealth.net/#/ Endeavour IM2]
|-
|Expression (Snomed CT)
|A combination of concepts that represent something that is not already represented as a Concept or Cluster. It typically links things across concepts (disease of infection (finding), with bacteria name (organism), with sepsis (observable entity) with kidney (body structure).
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/SNOMED+CT+expression Snomed CT Expression]
|-
|Expression Constraint Language
|The ''Expression Constraint Language'' is a formal syntax for representing SNOMED CT expression constraints. Expression constraints are computable rules used to define a bounded sets of clinical meanings represented by either precoordinated or postcoordinated expressions. Expression constraints can be used to restrict the valid values for a data element in an EHR, as the intensional definition of a concept-based reference set, as a machine processable query that identifies a set of matching expressions, or as a constraint that restricts the range of an attribute defined in the SNOMED CT concept model.
|Expression Constraint [https://confluence.ihtsdotools.org/display/DOCECL Language]
|-
|Facts
|Patient "Facts" - A concept introduced by NEL CSU to include '''1) Clinical components''' - effectively cohorts or registers - for instance long term conditions with national (e.g. QOF) or locally defined definitions e.g. Hypertension, Asthma '''2) Segmentation models''' e.g. "frailty" or likelihood of admission. '''3) Care quality standard compliance''' - quality measures which identify whether care standards are being met i.e. has a medication review or health check been done? Has the patient had each of 8 care processes for diabetes? Has the patient on a hypertension register had a blood pressure scheck and met the target?
Developing these at a London scale might be an excellent way to promote standardised Population Health Management.
|
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|
|-
|Functional Requirements
|These are product features or functionality that must be developed in order for the system to satisfy requirements. They must be implemented in the system in order for it to achieve what it is supposed to accomplish. Examples include "users should be able to download data based on their filtered search criteria" or "only admin-level users should be able to reset passwords for other users"
|[[wikipedia:Functional_requirement|Functional requirement]]
|-
|GitHub
|GitHub is a provider of version control and source-code management using [[wikipedia:Git|Git]] (which is software used for tracking changes in files). It enables software developers to safely store source code and work on it collaboratively, merging changes from different developers into the main "production" branch safely. It provides additional features such as bug/issue tracking, continuous integration, feature requests etc. GitHub was acquired by Microsoft in 2018.
|[[wikipedia:GitHub|GitHub]]
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|
|-
|HDRUK Phenotype Library
|This is a comprehensive, open access resource which exists to provide the research community with information, tools, and phenotyping algorithms for UK electronic health records (EHRs)
|[https://phenotypes.healthdatagateway.org/ HDR UK Phenotype Library]
|-
|Information Model
|A model describing ontologically standardised data items following a specific database schema within a specific relational database architecture.
It is an abstract model, showing the constraints, relationships, concepts, and data items for a domain.

The Discovery Information Model is a representation of the meaning and structure of data held in the electronic records of the health and social care sector. It includes libraries of queries, value sets, concept sets, data set definitions and mappings. The main purpose is to bridge the chasm that exists between highly technical digital representations and plain language so that when questions are asked of data, a lay person could use plain language without prior knowledge of the underlying models.
|[[wikipedia:Information_model|Information Model]]

[[Discovery health information model|DDS IM]]

[https://im.endeavourhealth.net/#/ IM Viewer]

[https://blog.softwaresuperglue.com/2018/11/09/information-model-vs-data-model/ Information Model vs Data Model]
|-
|Integrated Care Board (ICB)
|The ICB is a legal entity created in the Health and Care Act 2022. ICBs will bring the NHS together locally to deliver shared priorities, with a greater emphasis on collaboration and shared responsibility for the health of the local population. This will require governance arrangements that support collective accountability between partner organisations for whole-system delivery and performance. These arrangements should be proportionate, and they must facilitate transparent decision-making and foster the culture and behaviours that enable system working.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
[https://www.legislation.gov.uk/ukpga/2022/31/contents/enacted Health and Care Act 2022]
|-
|Integrated Care Partnership (ICP)
|The ICP is a joint committee of the ICB and the upper tier local authorities that are wholly or partly in the ICB area. From 1 July, the ICB and the local authorities will be under a legal duty to establish the ICP.
It is the role of the ICP to develop and publish the integrated care strategy for the ICB area, in particular focusing on how health and care can better integrate.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
|-
|Integrated Care System (ICS)
|Integrated care systems (ICSs) are partnerships of health and care organisations that come together to plan and deliver joined up services and to improve the health of people who live and work in their area.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
|-
|Joint Controller Agreement (JCA)
|The arrangements by which a group of data controllers agree to work together to agree the uses of data and how they jointly take responsibility for that. This is frequently manifested as the development of a representative committee that reviews the five safes, the legal basis for use of personal data, the worthiness of the proposed use of data
|[https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/controllers-and-processors/what-does-it-mean-if-you-are-joint-controllers/ Joint Controllers]
|-
|Knowledge Graph
|An information architecture where concepts are represented as nodes and edges in a network of relationships. This is usually represented in a NoSQL architecture designed to around relationships between concepts.
|
|-
|[[Logical Data Model]]
|The logical data model (or logical schema) describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|[[wikipedia:Logical_schema|Logical Schema]]
|-
|London Care Record
|A shared care record used for individual care. It is based on Cerner Health Information Exchange architecture.
|
|-
|Mauro Data Mapper
|Mauro Data Mapper is a third-party toolkit for the design and documentation of databases, data flows, and data standards, as well as related software artefacts such as data schemas and data forms. It was originally developed for the description of data in clinical research.
|[https://maurodatamapper.github.io/about/introduction/ Mauro Data Mapper]
|-
|Medical Classification
|Medical Classifications transform descriptions of procedures or diagnoses into standardised codes through the process of clinical coding. E.g., ICD10, OPCS4, LOINC. These
can also be called code sets. Hence, we call them a medical classification to avoid confusion with Clusters
|[[wikipedia:Medical_classification|Medical Classification]]
|-
|Metadata Library
|A tool that allows management of clusters
|
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|[https://digital.nhs.uk/services/terminology-servers NHSD Terminology Server]
|-
|Non-functional Requirements
|These are requirements concerning the operation of a system rather than functionality, and typically relate to security, scalability, performance, reliability etc. Examples include "the page must load within 500ms" or "the website must conform to WCAG AA accessibility standards"
|[[wikipedia:Non-functional_requirement|Non-functional Requirements]]
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|[https://www.imohealth.com/ideas/article/data-normalization-working-toward-a-common-clinical-terminology/ Healthcare Normalisation]
[https://blog.clinicalarchitecture.com/what-is-data-normalization What is data normalisation?]
|-
|Normalisation (database)
|This is the process of structuring a relational database in accordance with a series of derived rules, called ''normal forms'', in order to reduce redundancy, duplication, and improve data integrity.
|[[wikipedia:Database_normalization|Normalisation]]
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|
|-
|OMOP Data Model
|OMOP stands for Observational Medical Outcomes Partnership, which was formed to inform the appropriate use of observational healthcare databases. OHDSI (Observational Health Data Sciences and Informatics) is a collaborative that now includes all of the original OMOP research investigators and will continue to develop tools using the OMOP common data model and vocabulary (OMOP is no longer and active programme). OMOP is cited in the UK Health Data Research paper on building Building (federated )[https://zenodo.org/record/5767586/files/211208%20Building%20TREs%20Paper%20v1.0.pdf Trusted Research Environments] (p22).
|[https://www.ohdsi.org/data-standardization/the-common-data-model/ OMOP]
[https://github.com/OHDSI/CommonDataModel/blob/v5.4.0/inst/ddl/5.4/sql_server/OMOPCDM_sql_server_5.4_ddl.sql OMOP Common Data Model (GitHub)]
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. The most currently used ontology is SNOMED CT - the DDS ontology is made up of several ontologies (such as READ, CTV3 etc) mapped to SNOMED where appropriate SNOMED terms already exist, and extended where they don't.
A representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse (e.g., medicine). Example: Snomed CT.
|[[wikipedia:Ontology_(information_science)|Ontology]]
[https://termbrowser.nhs.uk/ NHSD SNOMED browser]

[https://www.snomed.org/ SNOMED.org]

[https://www.england.nhs.uk/digitaltechnology/digital-primary-care/snomed-ct/ SNOMED - NHS England]
|-
|OpenCodelists
|OpenCodelists was created by OpenSAFELY for creating and sharing codelists. See "Cluster" definition above.
|[https://www.opencodelists.org/ OpenCodelists]
[https://www.opensafely.org/ OpenSAFELY]
|-
|PESTLE Analysis
|A PESTLE analysis studies the key external factors (Political, Economic, Sociological, Technological, Legal and Environmental) that influence an organisation. It can be used in a range of different scenarios, and can guide people professionals and senior managers in strategic decision-making
|[https://www.cipd.co.uk/knowledge/strategy/organisational-development/pestle-analysis-factsheet Pestle Analysis]
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|[[wikipedia:Physical_schema|Physical Schema]]
|-
|Primary Care Reference Set
|This is a cluster of codes used within business rules authored and maintained by NHSD's primary care domain.
|[https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/quality-and-outcomes-framework-qof/quality-and-outcome-framework-qof-business-rules/primary-care-domain-reference-set-portal NHSD Primary Care Domain Reference Set Portal]
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SNOMED CT Concept
|A clinical idea to which a unique concept identifier has been assigned.
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/concept SNOMED CT Concept]
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|[[wikipedia:SQL|Structured Query Language]]
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|
|-
|Star Schema
|A simple database schema which consists of fact tables and dimension tables
|[[wikipedia:Star_schema|Star schema]]
|-
|Trusted Data Environment
|Raw [health and care] data is not powerful on its own. It must be shaped, checked, and curated into shape. It must be housed, and managed securely. It must be analysed. And then it must be communicated, and acted upon. That work all requires people, with modern data skills, in teams, using platforms that protect patients’ privacy and avoid needless duplication of effort."
|[https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1067053/goldacre-review-using-health-data-for-research-and-analysis.pdf Better, Broader, Safer: Using Health Data for Research and Analysis]
|-
|Trusted Research Environment
|An approach to data access based primarily around Trusted (Trustworthy) Research Environments (TREs), a type of Secure Data Environment; with
appropriate robust and independent accreditation, monitoring and auditing.
|[https://zenodo.org/record/5767586/files/211208%20Building%20TREs%20Paper%20v1.0.pdf Building Trusted Research Environments. Principles and Best Practices; towards TRE ecosystems.]
|-
|Value Set
|A generic term for a set of values, or codes, which might be stored in a metadata library or other set of lookup data. Value sets are used as the filters of data to create Data Sets - for example, in the case of populating the London Care Record from DDS, Value Sets are used to filter the underlying data in order to create a Data Set which is suitable for ingestion.
Concepts and Clusters are types of Value Set.

Code Set is sometimes used interchangeably with Value Set
|
|}

Glossary

2022-06-17T12:25:23Z

SimonMeredith: added canonical dm

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary. Please let us know if you would like to challenge or add definitions. simon.meredith@nhs.net or jack.barker@nhs.net
{| class="wikitable sortable"
|+
!Term
!Definition
!External Links
|-
|Canonical Data Model
|This is a type of data model that presents data entities and relationships in the simplest possible form. It is a design pattern which allows communication between different data formats. ''Canonical'' form describes the simplest representation of an object.
|[[wikipedia:Canonical_model|Canonical model - Wikipedia]]
|-
|Categories
|Broad sections of an Ontology or Classification such as the Categories in ICD 10 which contain all ICD 10 codes related to one body part. It might be also be large groupings of test results e.g. Haematology, Biochemistry, Immunology, Microbiology etc.
|[https://icd.who.int/browse10/2010/en#/II ICD 10]
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|[[wikipedia:Cloud_computing|Cloud Computing]]
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|[[wikipedia:Cloud_native_computing|Cloud Native Computing]]
|-
|Cluster
|A group of concept codes making another idea. The Cluster may be made up of concepts from different ontologies or classifications e.g., Snomed CT and ICD10. Synonyms for Cluster are refset, value set, codelist, code set, grouper. The name "cluster" was strongly advocated for by John Robson ([https://www.qmul.ac.uk/blizard/ceg/ Clinical Effectiveness Group] (CEG) NEL) because of his work in primary care.
|[http://app.powerbi.com/view?r=eyJrIjoiZTY0ODY0YzEtMjhhYy00ZTViLWJjZDQtMWZjOWVkMTZlOGExIiwidCI6IjUwZjYwNzFmLWJiZmUtNDAxYS04ODAzLTY3Mzc0OGU2MjllMiIsImMiOjh9 Primary Care Domain Reference Set Portal]
|-
|Common Data Model
|This is the shared Data Model which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
It defines the transformation of data from multiple different databases with potentially multiple coding systems into a common storage format (data model) as well as a common representation (terminologies, vocabularies, coding schemes)
|[https://www.jamesserra.com/archive/2019/06/common-data-model/ Microsoft Example]
|-
|Common Data Model (Discovery)
|The Discovery Common Data Model is the schema of the Discovery Compass v2 databases which exist in the Discovery Core and are held as subscriber databases within ICS environments. The schema is [https://wiki.discoverydataservice.org/index.php?title=Remote_Subscriber_Database_(RSD)_Schema_(Compass_2) here]..... The schema is thought to be based on EMIS primary care system schema.
|
|-
|Concept
|A coded idea. For example, Name = Baker's Asthma, SNOMED CT code = 34015007
|
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|
|-
|Data Dictionary
|A centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format e.g. NHS Data Dictionary.
|[[wikipedia:Data_dictionary|Data Dictionary]]
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|[[wikipedia:Data_model|Data Model]]
[https://digital.nhs.uk/services/nhs-data-model-and-dictionary-service NHS Data Model and Dictionary Service]
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|
|-
|[https://ico.org.uk/for-organisations/guide-to-data-protection/ico-codes-of-practice/data-sharing-a-code-of-practice/data-sharing-agreements/ Data Sharing Agreement] (DSA) Framework
|The arrangements for storing and managing data sharing agreements to ensure transparency across controllers and processors. It is likely that the Data Usage Committees/Data Access Groups ([https://ico.org.uk/for-organisations/guide-to-data-protection/key-dp-themes/guidance-for-the-use-of-personal-data-in-political-campaigning-1/controllers-joint-controllers-and-processors/ Joint Controllers]) will generate and store a copy of the DSA, whilst processors and sub-processors will also need a copy.
|
|-
|Data Warehouse
|A data warehouse stores data for the main purpose of reporting and data analysis. They are central repositories which bring together disparate data sets into one place, to enable reporting at scale and pace.
|[[wikipedia:Data_warehouse|Data Warehouse]]
|-
|Dataset
|A '''data set''' (or '''dataset''') is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question. The data set lists values for each of the variables, such as for example height and weight of an object, for each member of the data set. Data sets can also consist of a collection of documents or files.

In the Discovery Data Service datasets may be generated as a result of queries and valueset filters on the internal relational database.
|[[wikipedia:Data_set|Dataset]]
|-
|Discovery Collaborative
|A collaboration of three integrated care systems (ICS) with the ambition to develop and maintain a data service. It arose out of NE London where GPs and academics were working with a charitable trust to develop a secure, trusted, aggregated information resource to support quality improvement and research. As part of the national Local Health and Care Record Exemplar programme it expanded to NW and SE London ICSs. The Collaborative has a Board and working groups. The Chair of the Board reports to the Senior Responsible Officer for digital in each of the three ICSs.
|
|-
|Discovery Data Service (DDS)
|The hardware and software made available by the Discovery Collaborative. The DDS is not an organisation. The management and development of the bulk of the DDS is currently sub-contracted to a private company Voror in an Amazon Web Service Environment. However, there have been discussions about in-housing the service under closer control by NEL CCG and moving the service to a Microsoft Azure environment.
|
|-
|Discovery Health Information Model
|The "toolkit " of the Discovery Data Service. 1) A set of ontologies and classifications 2) A common data model 3) A metadata library of clusters of codes (codesets, valuesets etc) - currently the IM1 data tables in the DDS that are managed through IM2 (see below) 4) A catalogue of reference data (such as geographical areas, organisations and people). 5) A library of queries 6) A set of “maps” (mappings) 7) An Information Manager - currently (Endeavour Information Manager 2 - IM2) - an open source set of utilities that can be used to browse, search, or maintain the model.
|[https://wiki.endeavourhealth.org/index.php?title=Discovery_health_information_model Discovery Health Information Model]
|-
|Endeavour Information (Model) Manager
|The Information Model Manager is an application developed by the Endeavour Charitable Trust which provides a view of the Common Information Model and supports authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|[https://im.endeavourhealth.net/#/ Endeavour IM2]
|-
|Expression (Snomed CT)
|A combination of concepts that represent something that is not already represented as a Concept or Cluster. It typically links things across concepts (disease of infection (finding), with bacteria name (organism), with sepsis (observable entity) with kidney (body structure).
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/SNOMED+CT+expression Snomed CT Expression]
|-
|Expression Constraint Language
|The ''Expression Constraint Language'' is a formal syntax for representing SNOMED CT expression constraints. Expression constraints are computable rules used to define a bounded sets of clinical meanings represented by either precoordinated or postcoordinated expressions. Expression constraints can be used to restrict the valid values for a data element in an EHR, as the intensional definition of a concept-based reference set, as a machine processable query that identifies a set of matching expressions, or as a constraint that restricts the range of an attribute defined in the SNOMED CT concept model.
|Expression Constraint [https://confluence.ihtsdotools.org/display/DOCECL Language]
|-
|Facts
|Patient "Facts" - A concept introduced by NEL CSU to include '''1) Clinical components''' - effectively cohorts or registers - for instance long term conditions with national (e.g. QOF) or locally defined definitions e.g. Hypertension, Asthma '''2) Segmentation models''' e.g. "frailty" or likelihood of admission. '''3) Care quality standard compliance''' - quality measures which identify whether care standards are being met i.e. has a medication review or health check been done? Has the patient had each of 8 care processes for diabetes? Has the patient on a hypertension register had a blood pressure scheck and met the target?
Developing these at a London scale might be an excellent way to promote standardised Population Health Management.
|
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|
|-
|Functional Requirements
|These are product features or functionality that must be developed in order for the system to satisfy requirements. They must be implemented in the system in order for it to achieve what it is supposed to accomplish. Examples include "users should be able to download data based on their filtered search criteria" or "only admin-level users should be able to reset passwords for other users"
|[[wikipedia:Functional_requirement|Functional requirement]]
|-
|GitHub
|GitHub is a provider of version control and source-code management using [[wikipedia:Git|Git]] (which is software used for tracking changes in files). It enables software developers to safely store source code and work on it collaboratively, merging changes from different developers into the main "production" branch safely. It provides additional features such as bug/issue tracking, continuous integration, feature requests etc. GitHub was acquired by Microsoft in 2018.
|[[wikipedia:GitHub|GitHub]]
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|
|-
|HDRUK Phenotype Library
|This is a comprehensive, open access resource which exists to provide the research community with information, tools, and phenotyping algorithms for UK electronic health records (EHRs)
|[https://phenotypes.healthdatagateway.org/ HDR UK Phenotype Library]
|-
|Information Model
|A model describing ontologically standardised data items following a specific database schema within a specific relational database architecture.
It is an abstract model, showing the constraints, relationships, concepts, and data items for a domain.

The Discovery Information Model is a representation of the meaning and structure of data held in the electronic records of the health and social care sector. It includes libraries of queries, value sets, concept sets, data set definitions and mappings. The main purpose is to bridge the chasm that exists between highly technical digital representations and plain language so that when questions are asked of data, a lay person could use plain language without prior knowledge of the underlying models.
|[[wikipedia:Information_model|Information Model]]

[[Discovery health information model|DDS IM]]

[https://im.endeavourhealth.net/#/ IM Viewer]

[https://blog.softwaresuperglue.com/2018/11/09/information-model-vs-data-model/ Information Model vs Data Model]
|-
|Integrated Care Board (ICB)
|The ICB is a legal entity created in the Health and Care Act 2022. ICBs will bring the NHS together locally to deliver shared priorities, with a greater emphasis on collaboration and shared responsibility for the health of the local population. This will require governance arrangements that support collective accountability between partner organisations for whole-system delivery and performance. These arrangements should be proportionate, and they must facilitate transparent decision-making and foster the culture and behaviours that enable system working.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
[https://www.legislation.gov.uk/ukpga/2022/31/contents/enacted Health and Care Act 2022]
|-
|Integrated Care Partnership (ICP)
|The ICP is a joint committee of the ICB and the upper tier local authorities that are wholly or partly in the ICB area. From 1 July, the ICB and the local authorities will be under a legal duty to establish the ICP.
It is the role of the ICP to develop and publish the integrated care strategy for the ICB area, in particular focusing on how health and care can better integrate.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
|-
|Integrated Care System (ICS)
|Integrated care systems (ICSs) are partnerships of health and care organisations that come together to plan and deliver joined up services and to improve the health of people who live and work in their area.
|[https://www.england.nhs.uk/wp-content/uploads/2021/06/B1551--Guidance-to-Clinical-Commissioning-Groups-on-the-preparation-of-Integrated-Care-Board-constitutions.pdf Guidance to clinical commissioning groups on preparing integrated care board constitutions]
|-
|Joint Controller Agreement (JCA)
|The arrangements by which a group of data controllers agree to work together to agree the uses of data and how they jointly take responsibility for that. This is frequently manifested as the development of a representative committee that reviews the five safes, the legal basis for use of personal data, the worthiness of the proposed use of data
|[https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/controllers-and-processors/what-does-it-mean-if-you-are-joint-controllers/ Joint Controllers]
|-
|Knowledge Graph
|An information architecture where concepts are represented as nodes and edges in a network of relationships. This is usually represented in a NoSQL architecture designed to around relationships between concepts.
|
|-
|[[Logical Data Model]]
|The logical data model (or logical schema) describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|[[wikipedia:Logical_schema|Logical Schema]]
|-
|London Care Record
|A shared care record used for individual care. It is based on Cerner Health Information Exchange architecture.
|
|-
|Mauro Data Mapper
|Mauro Data Mapper is a third-party toolkit for the design and documentation of databases, data flows, and data standards, as well as related software artefacts such as data schemas and data forms. It was originally developed for the description of data in clinical research.
|[https://maurodatamapper.github.io/about/introduction/ Mauro Data Mapper]
|-
|Medical Classification
|Medical Classifications transform descriptions of procedures or diagnoses into standardised codes through the process of clinical coding. E.g., ICD10, OPCS4, LOINC. These
can also be called code sets. Hence, we call them a medical classification to avoid confusion with Clusters
|[[wikipedia:Medical_classification|Medical Classification]]
|-
|Metadata Library
|A tool that allows management of clusters
|
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|[https://digital.nhs.uk/services/terminology-servers NHSD Terminology Server]
|-
|Non-functional Requirements
|These are requirements concerning the operation of a system rather than functionality, and typically relate to security, scalability, performance, reliability etc. Examples include "the page must load within 500ms" or "the website must conform to WCAG AA accessibility standards"
|[[wikipedia:Non-functional_requirement|Non-functional Requirements]]
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|[https://www.imohealth.com/ideas/article/data-normalization-working-toward-a-common-clinical-terminology/ Healthcare Normalisation]
[https://blog.clinicalarchitecture.com/what-is-data-normalization What is data normalisation?]
|-
|Normalisation (database)
|This is the process of structuring a relational database in accordance with a series of derived rules, called ''normal forms'', in order to reduce redundancy, duplication, and improve data integrity.
|[[wikipedia:Database_normalization|Normalisation]]
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|
|-
|OMOP Data Model
|OMOP stands for Observational Medical Outcomes Partnership, which was formed to inform the appropriate use of observational healthcare databases. OHDSI (Observational Health Data Sciences and Informatics) is a collaborative that now includes all of the original OMOP research investigators and will continue to develop tools using the OMOP common data model and vocabulary (OMOP is no longer and active programme). OMOP is cited in the UK Health Data Research paper on building Building (federated )[https://zenodo.org/record/5767586/files/211208%20Building%20TREs%20Paper%20v1.0.pdf Trusted Research Environments] (p22).
|[https://www.ohdsi.org/data-standardization/the-common-data-model/ OMOP]
[https://github.com/OHDSI/CommonDataModel/blob/v5.4.0/inst/ddl/5.4/sql_server/OMOPCDM_sql_server_5.4_ddl.sql OMOP Common Data Model (GitHub)]
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. The most currently used ontology is SNOMED CT - the DDS ontology is made up of several ontologies (such as READ, CTV3 etc) mapped to SNOMED where appropriate SNOMED terms already exist, and extended where they don't.
A representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse (e.g., medicine). Example: Snomed CT.
|[[wikipedia:Ontology_(information_science)|Ontology]]
[https://termbrowser.nhs.uk/ NHSD SNOMED browser]

[https://www.snomed.org/ SNOMED.org]

[https://www.england.nhs.uk/digitaltechnology/digital-primary-care/snomed-ct/ SNOMED - NHS England]
|-
|OpenCodelists
|OpenCodelists was created by OpenSAFELY for creating and sharing codelists. See "Cluster" definition above.
|[https://www.opencodelists.org/ OpenCodelists]
[https://www.opensafely.org/ OpenSAFELY]
|-
|PESTLE Analysis
|A PESTLE analysis studies the key external factors (Political, Economic, Sociological, Technological, Legal and Environmental) that influence an organisation. It can be used in a range of different scenarios, and can guide people professionals and senior managers in strategic decision-making
|[https://www.cipd.co.uk/knowledge/strategy/organisational-development/pestle-analysis-factsheet Pestle Analysis]
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|[[wikipedia:Physical_schema|Physical Schema]]
|-
|Primary Care Reference Set
|This is a cluster of codes used within business rules authored and maintained by NHSD's primary care domain.
|[https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/quality-and-outcomes-framework-qof/quality-and-outcome-framework-qof-business-rules/primary-care-domain-reference-set-portal NHSD Primary Care Domain Reference Set Portal]
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SNOMED CT Concept
|A clinical idea to which a unique concept identifier has been assigned.
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/concept SNOMED CT Concept]
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|[[wikipedia:SQL|Structured Query Language]]
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|
|-
|Trusted Data Environment
|Raw [health and care] data is not powerful on its own. It must be shaped, checked, and curated into shape. It must be housed, and managed securely. It must be analysed. And then it must be communicated, and acted upon. That work all requires people, with modern data skills, in teams, using platforms that protect patients’ privacy and avoid needless duplication of effort."
|[https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1067053/goldacre-review-using-health-data-for-research-and-analysis.pdf Better, Broader, Safer: Using Health Data for Research and Analysis]
|-
|Trusted Research Environment
|An approach to data access based primarily around Trusted (Trustworthy) Research Environments (TREs), a type of Secure Data Environment; with
appropriate robust and independent accreditation, monitoring and auditing.
|[https://zenodo.org/record/5767586/files/211208%20Building%20TREs%20Paper%20v1.0.pdf Building Trusted Research Environments. Principles and Best Practices; towards TRE ecosystems.]
|-
|Value Set
|A generic term for a set of values, or codes, which might be stored in a metadata library or other set of lookup data. Value sets are used as the filters of data to create Data Sets - for example, in the case of populating the London Care Record from DDS, Value Sets are used to filter the underlying data in order to create a Data Set which is suitable for ingestion.
Concepts and Clusters are types of Value Set.

Code Set is sometimes used interchangeably with Value Set
|
|}

Glossary

2022-05-24T12:59:13Z

SimonMeredith: Added value set

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary. Please let us know if you would like to challenge or add definitions. simon.meredith@nhs.net or jack.barker@nhs.net
{| class="wikitable sortable"
|+
!Term
!Definition
!External Links
|-
|Categories
|Broad sections of an Ontology or Classification such as the Categories in ICD 10 which contain all ICD 10 codes related to one body part. It might be also be large groupings of test results e.g. Haematology, Biochemistry, Immunology, Microbiology etc.
|[https://icd.who.int/browse10/2010/en#/II ICD 10]
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|[[wikipedia:Cloud_computing|Cloud Computing]]
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|[[wikipedia:Cloud_native_computing|Cloud Native Computing]]
|-
|Cluster
|A group of concept codes making another idea. The Cluster may be made up of concepts from different ontologies or classifications e.g., Snomed CT and ICD10. Synonyms for Cluster are refset, value set, codelist, code set, grouper. The name "cluster" was strongly advocated for by John Robson ([https://www.qmul.ac.uk/blizard/ceg/ Clinical Effectiveness Group] (CEG) NEL) because of his work in primary care.
|[http://app.powerbi.com/view?r=eyJrIjoiZTY0ODY0YzEtMjhhYy00ZTViLWJjZDQtMWZjOWVkMTZlOGExIiwidCI6IjUwZjYwNzFmLWJiZmUtNDAxYS04ODAzLTY3Mzc0OGU2MjllMiIsImMiOjh9 Primary Care Domain Reference Set Portal]
|-
|Common Data Model
|This is the shared Data Model which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
It defines the transformation of data from multiple different databases with potentially multiple coding systems into a common storage format (data model) as well as a common representation (terminologies, vocabularies, coding schemes)
|[https://www.jamesserra.com/archive/2019/06/common-data-model/ Microsoft Example]
|-
|Common Data Model (Discovery)
|The Discovery Common Data Model is the schema of the Discovery Compass v2 databases which exist in the Discovery Core and are held as subscriber databases within ICS environments. The schema is here..... The schema is based on ......
|
|-
|Concept
|A coded idea. For example, Name = Baker's Asthma, SNOMED CT code = 34015007
|
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|
|-
|Data Dictionary
|A centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format e.g. NHS Data Dictionary.
|[[wikipedia:Data_dictionary|Data Dictionary]]
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|[[wikipedia:Data_model|Data Model]]
[https://digital.nhs.uk/services/nhs-data-model-and-dictionary-service NHS Data Model and Dictionary Service]
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|
|-
|[https://ico.org.uk/for-organisations/guide-to-data-protection/ico-codes-of-practice/data-sharing-a-code-of-practice/data-sharing-agreements/ Data Sharing Agreement] (DSA) Framework
|The arrangements for storing and managing data sharing agreements to ensure transparency across controllers and processors. It is likely that the Data Usage Committees/Data Access Groups ([https://ico.org.uk/for-organisations/guide-to-data-protection/key-dp-themes/guidance-for-the-use-of-personal-data-in-political-campaigning-1/controllers-joint-controllers-and-processors/ Joint Controllers]) will generate and store a copy of the DSA, whilst processors and sub-processors will also need a copy.
|
|-
|Data Warehouse
|A data warehouse stores data for the main purpose of reporting and data analysis. They are central repositories which bring together disparate data sets into one place, to enable reporting at scale and pace.
|[[wikipedia:Data_warehouse|Data Warehouse]]
|-
|Discovery Collaborative
|A collaboration of three integrated care systems (ICS) with the ambition to develop and maintain a data service. It arose out of NE London where GPs and academics were working with a charitable trust to develop a secure, trusted, aggregated information resource to support quality improvement and research. As part of the national Local Health and Care Record Exemplar programme it expanded to NW and SE London ICSs. The Collaborative has a Board and working groups. The Chair of the Board reports to the Senior Responsible Officer for digital in each of the three ICSs.
|
|-
|Discovery Data Service (DDS)
|The hardware and software made available by the Discovery Collaborative. The DDS is not an organisation. The management and development of the bulk of the DDS is currently sub-contracted to a private company Voror in an Amazon Web Service Environment. However, there have been discussions about in-housing the service under closer control by NEL CCG and moving the service to a Microsoft Azure environment.
|
|-
|Discovery Health Information Model
|The "toolkit " of the Discovery Data Service. 1) A set of ontologies and classifications 2) A common data model 3) A metadata library of clusters of codes (codesets, valuesets etc) - currently the IM1 data tables in the DDS that are managed through IM2 (see below) 4) A catalogue of reference data (such as geographical areas, organisations and people). 5) A library of queries 6) A set of “maps” (mappings) 7) An Information Manager - currently (Endeavour Information Manager 2 - IM2) - an open source set of utilities that can be used to browse, search, or maintain the model.
|[https://wiki.endeavourhealth.org/index.php?title=Discovery_health_information_model Discovery Health Information Model]
|-
|Endeavour Information (Model) Manager
|The Information Model Manager is an application developed by the Endeavour Charitable Trust which provides a view of the Common Information Model and supports authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|[https://im.endeavourhealth.net/#/ Endeavour IM2]
|-
|Expression (Snomed CT)
|A combination of concepts that represent something that is not already represented as a Concept or Cluster. It typically links things across concepts (disease of infection (finding), with bacteria name (organism), with sepsis (observable entity) with kidney (body structure).
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/SNOMED+CT+expression Snomed CT Expression]
|-
|Expression Constraint Language
|The ''Expression Constraint Language'' is a formal syntax for representing SNOMED CT expression constraints. Expression constraints are computable rules used to define a bounded sets of clinical meanings represented by either precoordinated or postcoordinated expressions. Expression constraints can be used to restrict the valid values for a data element in an EHR, as the intensional definition of a concept-based reference set, as a machine processable query that identifies a set of matching expressions, or as a constraint that restricts the range of an attribute defined in the SNOMED CT concept model.
|Expression Constraint [https://confluence.ihtsdotools.org/display/DOCECL Language]
|-
|Facts
|Patient "Facts" - A concept introduced by NEL CSU to include '''1) Clinical components''' - effectively cohorts or registers - for instance long term conditions with national (e.g. QOF) or locally defined definitions e.g. Hypertension, Asthma '''2) Segmentation models''' e.g. "frailty" or likelihood of admission. '''3) Care quality standard compliance''' - quality measures which identify whether care standards are being met i.e. has a medication review or health check been done? Has the patient had each of 8 care processes for diabetes? Has the patient on a hypertension register had a blood pressure scheck and met the target?
Developing these at a London scale might be an excellent way to promote standardised Population Health Management.
|
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|
|-
|Functional Requirements
|These are product features or functionality that must be developed in order for the system to satisfy requirements. They must be implemented in the system in order for it to achieve what it is supposed to accomplish. Examples include "users should be able to download data based on their filtered search criteria" or "only admin-level users should be able to reset passwords for other users"
|[[wikipedia:Functional_requirement|Functional requirement]]
|-
|GitHub
|GitHub is a provider of version control and source-code management using [[wikipedia:Git|Git]] (which is software used for tracking changes in files). It enables software developers to safely store source code and work on it collaboratively, merging changes from different developers into the main "production" branch safely. It provides additional features such as bug/issue tracking, continuous integration, feature requests etc. GitHub was acquired by Microsoft in 2018.
|[[wikipedia:GitHub|GitHub]]
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|
|-
|HDRUK Phenotype Library
|This is a comprehensive, open access resource which exists to provide the research community with information, tools, and phenotyping algorithms for UK electronic health records (EHRs)
|[https://phenotypes.healthdatagateway.org/ HDR UK Phenotype Library]
|-
|Information Model
|A model describing ontologically standardised data items following a specific database schema within a specific relational database architecture.
It is an abstract model, showing the constraints, relationships, concepts, and data items for a domain.

The Discovery Information Model is a representation of the meaning and structure of data held in the electronic records of the health and social care sector. It includes libraries of queries, value sets, concept sets, data set definitions and mappings. The main purpose is to bridge the chasm that exists between highly technical digital representations and plain language so that when questions are asked of data, a lay person could use plain language without prior knowledge of the underlying models.
|[[wikipedia:Information_model|Information Model]]

[[Discovery health information model|DDS IM]]

[https://im.endeavourhealth.net/#/ IM Viewer]

[https://blog.softwaresuperglue.com/2018/11/09/information-model-vs-data-model/ Information Model vs Data Model]
|-
|Joint Controller Agreement (JCA)
|The arrangements by which a group of data controllers agree to work together to agree the uses of data and how they jointly take responsibility for that. This is frequently manifested as the development of a representative committee that reviews the five safes, the legal basis for use of personal data, the worthiness of the proposed use of data
|[https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/controllers-and-processors/what-does-it-mean-if-you-are-joint-controllers/ Joint Controllers]
|-
|Knowledge Graph
|An information architecture where concepts are represented as nodes and edges in a network of relationships. This is usually represented in a NoSQL architecture designed to around relationships between concepts.
|
|-
|[[Logical Data Model]]
|The logical data model (or logical schema) describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|[[wikipedia:Logical_schema|Logical Schema]]
|-
|London Care Record
|A shared care record used for individual care. It is based on Cerner Health Information Exchange architecture.
|
|-
|Mauro Data Mapper
|Mauro Data Mapper is a third-party toolkit for the design and documentation of databases, data flows, and data standards, as well as related software artefacts such as data schemas and data forms. It was originally developed for the description of data in clinical research.
|[https://maurodatamapper.github.io/about/introduction/ Mauro Data Mapper]
|-
|Medical Classification
|Medical Classifications transform descriptions of procedures or diagnoses into standardised codes through the process of clinical coding. E.g., ICD10, OPCS4, LOINC. These
can also be called code sets. Hence, we call them a medical classification to avoid confusion with Clusters
|[[wikipedia:Medical_classification|Medical Classification]]
|-
|Metadata Library
|A tool that allows management of clusters
|
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|[https://digital.nhs.uk/services/terminology-servers NHSD Terminology Server]
|-
|Non-functional Requirements
|These are requirements concerning the operation of a system rather than functionality, and typically relate to security, scalability, performance, reliability etc. Examples include "the page must load within 500ms" or "the website must conform to WCAG AA accessibility standards"
|[[wikipedia:Non-functional_requirement|Non-functional Requirements]]
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|[https://www.imohealth.com/ideas/article/data-normalization-working-toward-a-common-clinical-terminology/ Healthcare Normalisation]
[https://blog.clinicalarchitecture.com/what-is-data-normalization What is data normalisation?]
|-
|Normalisation (database)
|This is the process of structuring a relational database in accordance with a series of derived rules, called ''normal forms'', in order to reduce redundancy, duplication, and improve data integrity.
|[[wikipedia:Database_normalization|Normalisation]]
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|
|-
|OMOP Data Model
|OMOP stands for Observational Medical Outcomes Partnership, which was formed to inform the appropriate use of observational healthcare databases. OHDSI (Observational Health Data Sciences and Informatics) is a collaborative that now includes all of the original OMOP research investigators and will continue to develop tools using the OMOP common data model and vocabulary (OMOP is no longer and active programme). OMOP is cited in the UK Health Data Research paper on building Building (federated )[https://zenodo.org/record/5767586/files/211208%20Building%20TREs%20Paper%20v1.0.pdf Trusted Research Environments] (p22).
|[https://www.ohdsi.org/data-standardization/the-common-data-model/ OMOP]
[https://github.com/OHDSI/CommonDataModel/blob/v5.4.0/inst/ddl/5.4/sql_server/OMOPCDM_sql_server_5.4_ddl.sql OMOP Common Data Model (GitHub)]
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. The most currently used ontology is SNOMED CT - the DDS ontology is made up of several ontologies (such as READ, CTV3 etc) mapped to SNOMED where appropriate SNOMED terms already exist, and extended where they don't.
A representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse (e.g., medicine). Example: Snomed CT.
|[[wikipedia:Ontology_(information_science)|Ontology]]
[https://termbrowser.nhs.uk/ NHSD SNOMED browser]

[https://www.snomed.org/ SNOMED.org]

[https://www.england.nhs.uk/digitaltechnology/digital-primary-care/snomed-ct/ SNOMED - NHS England]
|-
|OpenCodelists
|OpenCodelists was created by OpenSAFELY for creating and sharing codelists. See "Cluster" definition above.
|[https://www.opencodelists.org/ OpenCodelists]
[https://www.opensafely.org/ OpenSAFELY]
|-
|PESTLE Analysis
|A PESTLE analysis studies the key external factors (Political, Economic, Sociological, Technological, Legal and Environmental) that influence an organisation. It can be used in a range of different scenarios, and can guide people professionals and senior managers in strategic decision-making
|[https://www.cipd.co.uk/knowledge/strategy/organisational-development/pestle-analysis-factsheet Pestle Analysis]
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|[[wikipedia:Physical_schema|Physical Schema]]
|-
|Primary Care Reference Set
|This is a cluster of codes used within business rules authored and maintained by NHSD's primary care domain.
|[https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/quality-and-outcomes-framework-qof/quality-and-outcome-framework-qof-business-rules/primary-care-domain-reference-set-portal NHSD Primary Care Domain Reference Set Portal]
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SNOMED CT Concept
|A clinical idea to which a unique concept identifier has been assigned.
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/concept SNOMED CT Concept]
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|[[wikipedia:SQL|Structured Query Language]]
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|
|-
|Trusted Data Environment
|Raw [health and care] data is not powerful on its own. It must be shaped, checked, and curated into shape. It must be housed, and managed securely. It must be analysed. And then it must be communicated, and acted upon. That work all requires people, with modern data skills, in teams, using platforms that protect patients’ privacy and avoid needless duplication of effort."
|[https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1067053/goldacre-review-using-health-data-for-research-and-analysis.pdf Better, Broader, Safer: Using Health Data for Research and Analysis]
|-
|Trusted Research Environment
|An approach to data access based primarily around Trusted (Trustworthy) Research Environments (TREs), a type of Secure Data Environment; with
appropriate robust and independent accreditation, monitoring and auditing.
|[https://zenodo.org/record/5767586/files/211208%20Building%20TREs%20Paper%20v1.0.pdf Building Trusted Research Environments. Principles and Best Practices; towards TRE ecosystems.]
|-
|Value Set
|A generic term for a set of values, or codes, which might be stored in a metadata library or other set of lookup data. Value sets are used as the filters of data to create Data Sets - for example, in the case of populating the London Care Record from DDS, Value Sets are used to filter the underlying data in order to create a Data Set which is suitable for ingestion.
Concepts and Clusters are types of Value Set.

Code Set is sometimes used interchangeably with Value Set
|
|}

Glossary

2022-05-06T10:27:13Z

SimonMeredith: edited IM entry

Publisher API HL7 Segments

2022-04-28T13:05:54Z

SimonMeredith: Changes to MRG:1, PID:3, EVN:6 - from Mike Taylor (Imperial)

== Introduction ==
Below are all of the segments that a DDS Publisher using the HL7 API can send. The definitions below are generic and hence are relatively permissive. In the context of a specific message the segment various segment definitions may be refined to take account of the data needed to complete the business function associated with the message. More detail can be found on the [[Publisher API HL7 Messages|HL7 API messages page]]

== Coded fields ==
Note that currently only a subset of the coded fields that are specified in the segments below are interpreted by DDS (see below). The remainder are left as-is once ingested in to DDS are are not processed further. In this latter case it is recommended that publishers take note of the suggested code sets as in the future it might be that these fields are interpreted by DDS and therefore a mapping exercise will need to be undertaken to map from publisher local codes to a core DDS code set.

* Primary Language - PID:15
* Religion - PID:17
* Ethnic Group - PID:22
* Admission Type - PV1:4
* Hospital Service - PV1:10
* Admit Source - PV1:14
* Patient Type - PV1:18
* Discharge Disposition - PV1:36
* Discharged to Location - PV1:37

== MSA - Message acknowledgement ==

=== Overview ===
The MSA segment contains information sent whilst acknowledging an inbound message. The following rules govern the nature of the acknowledgement -

* If the message could not be saved to the DDS message store then an AR (failure occurred, retry) acknowledgement code is returned.
* If the message is malformed, or fails sender, recipient or message type checks, or is missing a message control ID, or fails for an unexpected reason, an AE (failure occurred, move to next message) acknowledgement code is returned.
* If the message passes the above checks and is saved to the message store, an AA (success) acknowledgement code is returned.
* If the message receiver fails to send an acknowledgement (e.g. network outage, hardware failure etc), it is expected the sender will automatically re attempt to send the message.

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|MSA:1 - Acknowledgement code
|MSH:1.1
|ID
|R
|N
|Message type. Allowed values are from table [[Publisher API Code Sets#DDS-HL7v2-AckCode|DDS-HL7v2-AckCode]]
|
|-
|MSA:2 - Message control ID
|MSH:1.1
|ST
|R
|N
|Unique (to DDS) identifier assigned by DDS
|
|-
|-
|MSA:3 - Text message
|MSA:3.1
|ST
|O
|N
|Further describes an error condition (may not always be provided by DDS)
|
|}

== MSH - Message Header ==

=== Overview ===
The MSH segment defines the intent, source, destination, and some specifics of the syntax of a message

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|MSH.1 - Field Separator
|MSH:1.1
|ST
|R
|N
|Defines the character to be used as the separator in the message
|
|-
|MSH.2 - Encoding characters
|MSH:2.1
|ST
|R
|N
|Contains (in the following order) component separator, repetition separator, escape character, and subcomponent separator
|
|-
|MSH:3 - Sending application
|MSH:3.1
|ST
|R
|N
|Sending application name
|
|-
|MSH:4 - Sending facility
|MSH:4.1
|ST
|R
|N
|Sending facility name
|
|-
|MSH:5 - Receiving application
|MSH:5.1
|ST
|R
|N
|Receiving application name
|
|-
|MSH:6 - Receiving facility
|MSH:6.1
|ST
|R
|N
|Receiving facility name
|
|-
|MSH:7 - Message timestamp
|MSH:7.1
|DT
|R
|N
|Datetime that the sending system created the message
|
|-
|-
| rowspan="3" |MSH:9 - Message type
| -
| -
|R
|N
|
|
|-
|MSH:9.1
|ID
|R
|N
|Message type. Values SHOULD be from table [[Publisher API Code Sets#DDS-HL7v2-MessageType|DDS-HL7v2-MessageType]]
|
|-
|MSH:9.2
|ID
|R
|N
|Trigger event. Values SHOULD be from table [[Publisher API Code Sets#DDS-HL7v2-EventType|DDS-HL7v2-EventType]]
|
|-
|MSH:10 - Message control ID.
|MSH:10.1
|ST
|R
|N
|This must be unique for all messages sent to DDS by each publisher. However, DDS does not currently detect or reject duplicate messages based on this value, so it is important that each publisher enforces this themselves.
|
|-
|MSH:11 - Processing ID
|MSH:11.1
|PT
|O
|N
|In production MUST be "P"
|
|-
|MSH:12 - Message version
|MSH:12.1
|ID
|R
|N
|The version of HL7 that this message conforms to. MUST be "2.3"
|
|}

== EVN - Event Type ==

=== Overview ===
The EVN segment is used to communicate trigger event information to receiving applications

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|EVN:1 - Event Type Code
|EVN:1.1
|ID
|O
|N
|The value should be the same as theMSH:9.2 (trigger event)
|
|-
|EVN:2 - Recorded Date/Time
|EVN:2.1
|TS
|R
|N
|Timestamp of when the transaction was entered
|
|-
|EVN:3 - Date/Time planned event
|EVN:3.1
|TS
|O
|N
|Avoid populating this field. Instead use PV2 expected admit date and PV2 expected discharge date whenever possible.
|
|-
|EVN:4 - Event reason code
|EVN:4.1
|CWE
|O
|N
|The reason for this event. Allowed values are from table [[Publisher API Code Sets#0062-EventReason|0062-EventReason]]
|
|-
| rowspan="5" |EVN:5 - Operator ID
| -
| -
|O
|N
|Operator ID. If provided, at least the family name must be given.
|
|-
|EVN:5.2
|ST
|R
|N
|Family Name
|
|-
|EVN:5.3
|ST
|O
|N
|Given Name
|
|-
|EVN:5.4
|ST
|O
|N
|Middle Names
|
|-
|EVN:5.6
|ST
|O
|N
|Prefix
|
|-
|EVN:6 - Event occured
|EVN:6.1
|TS
|R
|N
|This field contains the date/time that the event actually occurred. For example, on a transfer (A02 (transfer a patient)), this field must contain the date/time the patient was actually transferred. On a cancellation event, this field should contain the date/time that the event being cancelled occurred.
|
|}

== PID - Patient Identification ==

=== Overview ===
The PID segment is used as the main way of communicating patient identification information. The majority of patient identifying and demographic information held within the PID segment is not subject to frequent changes

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|PID:2 - Patient ID
| -
| -
|X
|N
|Not used. Instead please populate PID.3 Patient Identifier List
|
|-
| rowspan="3" |PID:3 - Patient Identifier List
| -
| -
|R
|Y
|Patient Identifier List to include

Always:

* the patient's main local (to the publisher) identifier

Optionally:

* alternative MRNs
* the patient's NHS number
|
|-
|PID:3.1
|ST
|R
|Y
|Patient ID
|
|-
|PID:3.4
|ST
|R
|Y
|Assigning authority

* "MRN" - for the patient's main local (to the publisher) identifier
* "ALT_MRN" - for alternative MRNs
* "NHS" - for NHS Number
|<nowiki>PID|1| |01529577^^^MRN~4111111998^^^NHS~E02055831^^^ALT_MRN~M2600001^^^ALT_MRN</nowiki>
|-
| rowspan="5" |PID:5 - Patient Name
| -
| -
|R
|N
|
|
|-
|PID:5.1
|ST
|R
|N
|Family name
|
|-
|PID:5.2
|ST
|O
|N
|Given name
|
|-
|PID:5.3
|ST
|O
|N
|Middle names
|
|-
|PID:5.5
|ST
|O
|N
|Title
|
|-
|PID:7
|PID:7.1
|TS
|R
|N
|Date of birth
|
|-
|PID:8
|PID:8.1
|IS
|R
|N
|Administrative sex. If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from table [[Publisher API Code Sets#AdministrativeSex|AdministrativeSex]]
|
|-
| rowspan="8" |PID:11 - Patient Address
| -
| -
|O
|N
|
|
|-
|PID:11.1
|ST
|O
|N
|Address line 1
|
|-
|PID:11.2
|ST
|O
|N
|Address line 2
|
|-
|PID:11.3
|ST
|O
|N
|City
|
|-
|PID:11.5
|ST
|O
|N
|Post code
|
|-
|PID:11.6
|ID
|O
|N
|Country. Values SHOULD be from table [[Publisher API Code Sets#0399-CountryCode|0399-CountryCode]]
|
|-
|PID:11.7
|ID
|O
|N
|Address usage eg temporary address
|
|-
|PID:11.9
|IS
|O
|N
|County
|
|-
| rowspan="3" |PID:13 - Home contact information
| -
| -
|O
|N
|
|
|-
|PID:13.1
|TN
|O
|N
|Contact value
|
|-
|PID:13.2
|ID
|O
|N
|Contact use code. Allowed values are "home" or "mobile"
|
|-
| rowspan="3" |PID:14 - Work contact information
| -
| -
|O
|N
|
|
|-
|PID:14.1
|TN
|O
|N
|Contact value
|
|-
|PID:14.2
|ID
|O
|N
|Contact use code. Allowed values is "work"
|
|-
| rowspan="2" |PID:15 - Primary language
| -
| -
|O
|N
|Primary language. This should be the ISO 639-1 code or one of the five communication method extensions defined in the NHS Data Dictionary code set.
|
|-
|PID:15.1
|ST
|O
|N
|Primary language. If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source]<nowiki> then allowed values are child concepts of the SNOMED CT World Languages concept - 297289008 | World languages (qualifier value) |</nowiki>
|
|-
|PID:17 - Religion
|PID:17.1
|IS
|O
|N
|Religion. If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from [https://datadictionary.nhs.uk/attributes/religious_or_other_belief_system_affiliation_code.html RELIGIOUS OR OTHER BELIEF SYSTEM AFFILIATION CODE]
|
|-
|PID:18 - Patient Account Number
|PID:18.1
|ST
|C
|N
|Though officially this field is intended to capture account number it has been repurposed in DDS. It is used to carry the identifier for the episode of care. Simplistically an episode of care encapsulates all of the encounters that a patient may have with a healthcare organisation from the point of referral to the point of discharge.
If the publisher does not have the concept of episode of care then instead this MUST be the same value as that found in PV1.19 - Visit number.

If no PV1 segment accompanies this PID segment then PID:18 should not be populated
|
|-
|PID:22 - Ethnic group
| -
| -
|O
|N
|
|
|-
|
|PID:22.1
|ST
|O
|N
|Ethnicity identifier. If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from [https://datadictionary.nhs.uk/data_elements/ethnic_category.html?hl=ethnicity ETHNIC CATEGORY]
|
|-
|PID:29 - Patient Death Date and Time
|PID:29.1
|TS
|C
|N
|If PID.30:1 is not empty and PID:30.1 == "Y" then PID:29 is mandatory
|
|-
|PID:30 - Patient Death Indicator
|PID:30.1
|ID
|O
|N
|"Y" or "N"
|
|}

== PD1 - Patient Additional Demographic ==

=== Overview ===
The PD1 segment contains demographic information about a patient that is likely to change. In the case of DDS is it used to carry GP data.

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
| rowspan="3" |PD1:3 - Patient Primary Facility
| -
| -
|R
|N
|
|
|-
|PD1:3.1
|ST
|R
|N
|name
|
|-
|PD1:3.3
|ST
|R
|N
|ODS code
|
|-
| rowspan="5" |PD1:4 - Patient Primary Care Provider Name and ID No
| -
| -
|R
|N
|
|
|-
|PD1:4.1
|ST
|R
|N
|GMC code
|
|-
|PD1:4.2
|FN
|R
|N
|Family name
|
|-
|PD1:4.3
|ST
|R
|N
|Given name
|
|-
|PD1:4.6
|ST
|O
|N
|Prefix
|
|}
== MRG - Merge Patient Information ==

=== Overview ===
The MRG segment is used by DDS to support merging of patient identifiers

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|MRG:1 - Prior Patient Identifier List
| -
| -
| -
| -
|Patient Identifier List to include

Always

* the patient's main local (to the publisher) identifier

Optionally

* alternative MRNs
* the patient's NHS number
|
|-
|
|MRG:1.1
|CX
|R
|Y
|The patient's identifier
|
|-
|
|MRG:1.4
|CX
|R
|N
|Assigning authority

* “MRN” – for the patient's main local (to the publisher) identifier
* “ALT_MRN” – for alternative MRNs
* “NHS” for NHS number
|
|-
|MRG:2 - Prior Alternate Patient ID
|
|CX
|X
|N
|Ignored if supplied
|
|-
|MRG:3 - Prior Alternate Account Number
|
|CX
|X
|N
|Ignored if supplied
|
|-
|MRG:4 - Prior Patient ID
|
| -
|X
|N
|Ignored if supplied
|
|-
|MRG:5 - Prior Visit Number
|
|CX
|X
|N
|Ignored if supplied
|
|-
|MRG:6 - Prior Alternate Visit ID
|
|CX
|X
|N
|Ignored if supplied
|
|-
|MRG:7 - Prior Patient Name
| -
| XPN
|X
|N
|Ignored if supplied
|
|} 

== PV1 - Patient Visit ==

=== Overview ===
The PV1 segment is used by Registration/Patient Administration applications to communicate information on a visit-specific basis.

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|PV1:2 - Patient Class
|PV1:2.1
|ID
|R
|N
|If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from table [[Publisher API Code Sets#PatientClass|PatientClass]]
|
|-
|PV1:3 - Assigned Patient Location
|PV1:3.1
|IS
|R
|N
|General patient location
|
|-
|PV1:4 - Admission Type
|PV1:4.1
|ID
|O
|N
|If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from [https://datadictionary.nhs.uk/attributes/admission_method.html ADMISSION METHOD]
|
|-
| rowspan="5" |PV1:8 - Referring Doctor
| -
| -
|O
|N
|Referring doctor. If provided, at least the family name must be given.
|
|-
|PV1:8.1
|ST
|O
|N
|ID number
|
|-
|PV1:8.2
|ST
|O
|N
|Family Name
|
|-
|PV1:8.3
|ST
|O
|N
|Given Name
|
|-
|PV1:8.6
|ST
|O
|N
|Prefix
|
|-
| rowspan="5" |PV1:9 - Consulting Doctor
| -
| -
|R
|N
|Consulting doctor. If provided, at least the family name must be given.
|
|-
|PV1:9.1
|ST
|R
|N
|ID number
|
|-
|PV1:9.2
|ST
|R
|N
|Family Name
|
|-
|PV1:9.3
|ST
|O
|N
|Given Name
|
|-
|PV1:9.6
|ST
|O
|N
|Prefix
|
|-
|PV1:10 - Hospital Service
|PV1:10.1
|IS
|R
|N
|If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from [https://datadictionary.nhs.uk/attributes/treatment_function_code.html TREATMENT FUNCTION CODE]
|
|-
|PV1:14 - Admit source
|PV1:14.1
|IS
|O
|N
|If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from [https://datadictionary.nhs.uk/attributes/source_of_admission.html SOURCE OF ADMISSION]
|
|-
|PV1:18 - Patient Type
|PV1:18.1
|IS
|R
|N
|If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from table [[Publisher API Code Sets#HL7v3-EncounterType|HL7v3-EncounterType]]
|
|-
|PV1:19 - Visit Number
|PV1:19.1
|CX
|R
|N
|Visit ID. Should uniquely identify an Encounter from the DDS publisher.
|
|-
|PV1:36 - Discharge Disposition
|PV1:36.1
|IS
|O
|N
|If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from [https://datadictionary.nhs.uk/attributes/discharge_method.html DISCHARGE METHOD]
|
|-
|PV1:37 - Discharged to Location
|PV1:37.1
|ID
|O
|N
|If [https://wiki.discoverydataservice.org/index.php?title=Publisher_API_HL7_Messages#Coded_field_mapping mapping at source] then allowed values are from [https://datadictionary.nhs.uk/attributes/discharge_destination.html DISCHARGE DESTINATION]
|
|-
|PV1:44 - Admit Date/Time
|PV1:44.1
|TS
|C
|N
|Admit timestamp
|
|-
|PV1:45 - Discharge Date/Time
|PV1:45.1
|TS
|O
|N
|Discharge timestamp
|
|}

== NTE - Notes and Comments ==

=== Overview ===
The NTE segment is used to hold comments [TODO - are there any constraints on the size of an individual comment]

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|NTE:3 - Comment
|NTE:3.1
|FT
|R
|Y
|Comment
|
|}

== ORC - Common Order ==

=== Overview ===
The ORC segment is used to carry information that is common across an order

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|ORC:3 - Filler Order number
|ORC:3.1
|ST
|O
|N
|This string must uniquely identify an order from other orders in the filling system
|
|-
| rowspan="4" |ORC:21 - Ordering Facility name
| -
| -
|R
|N
|Used to differentiate between different sources of result data (eg GP vs acute settings)
|
|-
|ORC:21.1
|ST
|O
|N
|Facility name
|
|-
|ORC:21.3
|ST
|R
|N
|Facility ID. Note that this MUST be the ODS code of the ordering facility
|
|-
|ORC:21.7
|ID
|R
|N
|Facility ID type. Values SHOULD be from table [[Publisher API Code Sets#0074-DiagnosticServiceSectionID|0074-DiagnosticServiceSectionID]]
|
|}

== OBR - Observation Request ==

=== Overview ===
DDS interprets an OBR as either representing a single textual laboratory report or a collection of individual test results. More information can be found in the [[Publisher API HL7 Messages#ORU_R01:_Unsolicited_Observation_.28Laboratory.29|ORU R01]] - Unsolicited Observation definition.
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|OBR:3 - Filler Order number
|OBR:3.1
|ST
|C
|N
|OBR:3-filler order number is identical to ORC:3-filler order number.
If the filler order number is not present in the ORC, it must be present in the associated OBR
|
|-
| rowspan="5" |OBR:4 - Universal Service Identifier
| -
| -
|R
|N
|
|
|-
|OBR:4.1
|ST
|R
|N
|Service Identifier
|
|-
|OBR:4.2
|ST
|R
|N
|Service name
|
|-
|OBR:4.3
|ID
|R
|N
|Service coding system. Allowed values are [TODO]
|
|-
|OBR:4.5
|ST
|O
|N
|Alternative service name
|
|-
|OBR:7 - Observation date/time
|ORC:7.1
|TS
|C
|N
|Observation timestamp. If this is not present, OBX:14 is used instead. It is an error for neither to be provided.
|
|-
| rowspan="5" |OBR:16 - Ordered by
|
|
|O
|N
|If provided, at least the family name must be given.
|
|-
|OBR:16.2
|ST
|R
|N
|Family Name
|
|-
|OBR:16.3
|ST
|O
|N
|Given Name
|
|-
|OBR:16.4
|ST
|O
|N
|Middle Names
|
|-
|OBR:16.6
|ST
|O
|N
|Prefix
|
|-
|OBR:24 - Discipline
|OBR:24.1
|ID
|O
|N
|This field is the section of the diagnostic service where the observation was performed.
If the study was performed by an outside service, the identification of that service should be recorded here. Values SHOULD be from table [[Publisher API Code Sets#0074-DiagnosticServiceSectionID|0074-DiagnosticServiceSectionID]]
|
|-
|OBR:25 - Result status
|OBR:25.1
|ID
|O
|N
|Only values of F and C will be processed. Values of I, O, P or X (which indicate pending or no results) are silently ignored, whilst any other values will cause an error. Values SHOULD be from table [[Publisher API Code Sets#DDS-ResultStatus|DDS-ResultStatus]]
|
|}

=== Definition ===

== OBX - Observation or result ==

=== Overview ===
The OBX segment can carry an observation about the patient or it can also be used to carry test results.

=== Definition ===
{| class="wikitable"
!Field
!Component
!Data Type
!Optionality
!Repeating
!Description
!Example
|-
|OBX:1 - Set ID
|OBX:1.1
|SI
|R
|N
|Sequence number for OBX. Must be unique under its associated OBR segment
|
|-
|OBX:2 - Value Type
|OBX:2.1
|ID
|R
|N
|Values SHOULD be from table [[Publisher API Code Sets#0125-ValueType|0125-ValueType]]
|
|-
| rowspan="4" |OBX:3 - Observation Identifier
| -
| CWE
|R
|Y
|If this is a textual report, this value will be overridden by OBR:4. Otherwise this value must be provided.
|
|-
|OBX:3.1
|ST
|R
|N
|Test ID. If this is a textual report, this value will be overridden by OBR:4.1. Otherwise this value must be provided. If a measurement is being provided, this ID must match one of the predefined values which DDS can accept. At the time of writing the acceptable code systems are still being defined though there is an aspiration to adopt the [https://digital.nhs.uk/about-nhs-digital/our-work/nhs-digital-data-and-technology-standards/clinical-information-standards#unified-test-list National Unified Test List] where appropriate. Note that this does not exclude the adoption of more appropriate code systems depending upon the nature of the observation
|
|-
|OBX:3.2
|ST
|O
|N
|Test Name
|
|-
|OBX:3.3
|ST
|C
|N
|Test coding system. If this is a textual report, this value will be overridden by OBR:4.3.
|
|-
|OBX:5 - Observation Value
| OBX:5.1
| VARIES
|R
|N
|Value. Type MUST match OBX:2.1
|
|-
| rowspan="4" |OBX:6 - Observation Units
| -
| -
|O
|N
|
|
|-
|OBX:6.1
|ST
|R
|N
|Unit ID
|
|-
|OBX:6.2
|ST
|O
|N
|Unit Name
|
|-
|OBX:6.3
|ST
|R
|N
|Unit coding system
|
|-
|OBX:7
|OBX:7.1
|ST
|O
|N
|Reference range. Can be used to convey low value, high value and normal value. DDS will parse the data into one or more of these categories depending upon how the reference range value is formatted 

* [low value]"-"[high value]
* "<"[Number] (i.e. "less than number")
* ">"[Number] (i.e. "greater than number")
* "<="[Number] (i.e. "less than or equal to number")
* ">="[Number] (i.e. "greater than or equal to number")
* [Alphabetic value indicating normal value]
|See table below for examples
|-
| rowspan="4" |OBX:8 - Abnormal flag
| -
| -
|C
|N
|To flag the result as abnormal (e.g., High, Low) this flag MUST be sent in the message
|
|-
|OBX:8.1
|
|R
|N
|Flag ID - Values SHOULD be from table <nowiki>http://terminology.hl7.org/ValueSet/v3-ObservationInterpretation</nowiki>
|
|-
|OBX:8.2
|
|O
|
|Flag name
|
|-
|OBX:8.3
|
|O
|N
|Flag coding system - If present then SHOULD be "<nowiki>http://terminology.hl7.org/ValueSet/v3-ObservationInterpretation</nowiki>"
|
|-
|OBX:11
|ST
|O
|N
|Result status
|Result status. Only values of F and C will be processed. Values of I, O, P or X (which indicate pending or no results) are silently ignored, whilst any other values will cause an error. Values SHOULD be from table [[Publisher API Code Sets#DDS-ResultStatus|DDS-ResultStatus]]
|
|-
|OBX:13
|ST
|O
|N
|Custom access rules
|Custom access rules for lab results. Currently supported: "{patientDelay:NUMBERdays}" with any whole number in place of "NUMBER" (no spaces). The patient will see that a lab result has arrived, but the result value will not be revealed until the set number of days past the date/time of observation have passed. If this field is not specified the patient will be able to see their results immediately. Note: if this OBR group is a textual report spanning multiple OBX segments, the delay must be provided in the first.
|{patientDelay:3days}
|-
|OBX:14
|ST
|C
|N
|Date/Time of the Observation
|Observation timestamp. If this is not present, OBR:7 is used instead. It is an error for neither to be provided.
|
|
|-
| rowspan="5" |OBX:16 - Responsible Observer
|
|
|O
|N
|If provided, at least the family name must be given.
|
|-
|OBX:16.2
|ST
|R
|N
|Family Name
|
|-
|OBX:16.3
|ST
|O
|N
|Given Name
|
|-
|OBX:16.4
|ST
|O
|N
|Middle Names
|
|-
|OBX:16.6
|ST
|O
|N
|Prefix
|}

=== Reference range ===
{| class="wikitable"
!OBX:7 value
!Low value
!High value
!Normal value
|-
|"3-4"
|3
|4
| -
|-
|"3--4"
|3
| -4
| -
|-
|"-3-4"
| -3
|4
| -
|-
|"3 - 4"
|3
|4
| -
|-
|" 5"
| -
| -
|5
|-
|">5"
| -
| -
|">5"
|-
|"POS"
| -
| -
|"POS"
|}

Glossary

2022-04-28T11:34:47Z

SimonMeredith: added functional/non functional requirements

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary
{| class="wikitable sortable"
|+
!Term
!Definition
!External Links
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|[[wikipedia:Cloud_computing|Cloud Computing]]
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|[[wikipedia:Cloud_native_computing|Cloud Native Computing]]
|-
|Cluster
|A group of concept codes making another idea. The Cluster may be made up of concepts from different ontologies or classifications e.g., Snomed CT and ICD10. Synonyms for Cluster are refset, value set, codelist, code set, grouper. These can also be called code sets. They are called Clusters to avoid confusion with medical classifications such as ICD 10
|[http://app.powerbi.com/view?r=eyJrIjoiZTY0ODY0YzEtMjhhYy00ZTViLWJjZDQtMWZjOWVkMTZlOGExIiwidCI6IjUwZjYwNzFmLWJiZmUtNDAxYS04ODAzLTY3Mzc0OGU2MjllMiIsImMiOjh9 Primary Care Domain Reference Set Portal]
|-
|Common Data Model
|This is the shared Data Model which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
It defines the transformation of data from multiple different databases with potentially multiple coding systems into a common storage format (data model) as well as a common representation (terminologies, vocabularies, coding schemes)
|[https://www.jamesserra.com/archive/2019/06/common-data-model/ Microsoft Example]
|-
|Concept
|A coded idea. For example, Name = Baker's Asthma, SNOMED CT code = 34015007
|
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|
|-
|Data Dictionary
|A centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format e.g. NHS Data Dictionary.
|[[wikipedia:Data_dictionary|Data Dictionary]]
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|[[wikipedia:Data_model|Data Model]]
[https://digital.nhs.uk/services/nhs-data-model-and-dictionary-service NHS Data Model and Dictionary Service]
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|
|-
|Data Warehouse
|A data warehouse stores data for the main purpose of reporting and data analysis. They are central repositories which bring together disparate data sets into one place, to enable reporting at scale and pace.
|[[wikipedia:Data_warehouse|Data Warehouse]]
|-
|DDS Information Model Manager
|The Information Model Manager is an application developed by the Endeavour Charitable Trust which provides a view of the Common Information Model and supports authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|[https://wiki.endeavourhealth.org/index.php?title=Discovery_health_information_model Discovery Health Information Model]
|-
|Endeavour Information Model
|
|
|-
|Expression (Snomed CT)
|A combination of concepts that represent something that is not already represented as a Concept or Cluster. It typically links things across concepts (disease of infection (finding), with bacteria name (organism), with sepsis (observable entity) with kidney (body structure).
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/SNOMED+CT+expression Snomed CT Expression]
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|
|-
|Functional Requirements
|These are product features or functionality that must be developed in order for the system to satisfy requirements. They must be implemented in the system in order for it to achieve what it is supposed to accomplish. Examples include "users should be able to download data based on their filtered search criteria" or "only admin-level users should be able to reset passwords for other users"
|[[wikipedia:Functional_requirement|Functional requirement]]
|-
|GitHub
|GitHub is a provider of version control and source-code management using [[wikipedia:Git|Git]] (which is software used for tracking changes in files). It enables software developers to safely store source code and work on it collaboratively, merging changes from different developers into the main "production" branch safely. It provides additional features such as bug/issue tracking, continuous integration, feature requests etc. GitHub was acquired by Microsoft in 2018.
|[[wikipedia:GitHub|GitHub]]
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|
|-
|HDRUK Phenotype Library
|This is a comprehensive, open access resource which exists to provide the research community with information, tools, and phenotyping algorithms for UK electronic health records (EHRs)
|[https://phenotypes.healthdatagateway.org/ HDR UK Phenotype Library]
|-
|Information Model
|A model describing ontologically standardised data items following a specific database schema within a specific relational database architecture.
|
|-
|Knowledge Graph
|An information architecture where concepts are represented as nodes and edges in a network of relationships. This is usually represented in a NoSQL architecture designed to around relationships between concepts.
|
|-
|[[Logical Data Model]]
|The logical data model (or logical schema) describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|[[wikipedia:Logical_schema|Logical Schema]]
|-
|Mauro Data Mapper
|Mauro Data Mapper is a third-party toolkit for the design and documentation of databases, data flows, and data standards, as well as related software artefacts such as data schemas and data forms. It was originally developed for the description of data in clinical research.
|[https://maurodatamapper.github.io/about/introduction/ Mauro Data Mapper]
|-
|Medical Classification
|Medical Classifications transform descriptions of procedures or diagnoses into standardised codes through the process of clinical coding. E.g., ICD10, OPCS4, LOINC. These
can also be called code sets. Hence, we call them a medical classification to avoid confusion with Clusters
|[[wikipedia:Medical_classification|Medical Classification]]
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|[https://digital.nhs.uk/services/terminology-servers NHSD Terminology Server]
|-
|Non-functional Requirements
|These are requirements concerning the operation of a system rather than functionality, and typically relate to security, scalability, performance, reliability etc. Examples include "the page must load within 500ms" or "the website must conform to WCAG AA accessibility standards"
|[[wikipedia:Non-functional_requirement|Non-functional Requirements]]
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|[https://www.imohealth.com/ideas/article/data-normalization-working-toward-a-common-clinical-terminology/ Healthcare Normalisation]
[https://blog.clinicalarchitecture.com/what-is-data-normalization What is data normalisation?]
|-
|Normalisation (database)
|This is the process of structuring a relational database in accordance with a series of derived rules, called ''normal forms'', in order to reduce redundancy, duplication, and improve data integrity.
|[[wikipedia:Database_normalization|Normalisation]]
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|
|-
|OMOP Data Model
|OMOP stands for Observational Medical Outcomes Partnership, which was formed to inform the appropriate use of observational healthcare databases. OHDSI (Observational Health Data Sciences and Informatics) is a collaborative that now includes all of the original OMOP research investigators and will continue to develop tools using the OMOP common data model and vocabulary (OMOP is no longer and active programme).
|[https://www.ohdsi.org/data-standardization/the-common-data-model/ OMOP]
[https://github.com/OHDSI/CommonDataModel/blob/v5.4.0/inst/ddl/5.4/sql_server/OMOPCDM_sql_server_5.4_ddl.sql OMOP Common Data Model (GitHub)]
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. The most currently used ontology is SNOMED CT - the DDS ontology is made up of several ontologies (such as READ, CTV3 etc) mapped to SNOMED where appropriate SNOMED terms already exist, and extended where they don't.
A representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse (e.g., medicine). Example: Snomed CT.
|[[wikipedia:Ontology_(information_science)|Ontology]]
[https://termbrowser.nhs.uk/ NHSD SNOMED browser]

[https://www.snomed.org/ SNOMED.org]

[https://www.england.nhs.uk/digitaltechnology/digital-primary-care/snomed-ct/ SNOMED - NHS England]
|-
|OpenCodelists
|OpenCodelists was created by OpenSAFELY for creating and sharing codelists. See "Cluster" definition above.
|[https://www.opencodelists.org/ OpenCodelists]
[https://www.opensafely.org/ OpenSAFELY]
|-
|PESTLE Analysis
|A PESTLE analysis studies the key external factors (Political, Economic, Sociological, Technological, Legal and Environmental) that influence an organisation. It can be used in a range of different scenarios, and can guide people professionals and senior managers in strategic decision-making
|[https://www.cipd.co.uk/knowledge/strategy/organisational-development/pestle-analysis-factsheet Pestle Analysis]
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|[[wikipedia:Physical_schema|Physical Schema]]
|-
|Primary Care Reference Set
|This is a cluster of codes used within business rules authored and maintained by NHSD's primary care domain.
|[https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/quality-and-outcomes-framework-qof/quality-and-outcome-framework-qof-business-rules/primary-care-domain-reference-set-portal NHSD Primary Care Domain Reference Set Portal]
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SNOMED CT Concept
|A clinical idea to which a unique concept identifier has been assigned.
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/concept SNOMED CT Concept]
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|[[wikipedia:SQL|Structured Query Language]]
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|
|}

Glossary

2022-04-27T06:59:43Z

SimonMeredith: added normalisation links

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary
{| class="wikitable sortable"
|+
!Term
!Definition
!External Links
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|[[wikipedia:Cloud_computing|Cloud Computing]]
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|[[wikipedia:Cloud_native_computing|Cloud Native Computing]]
|-
|Cluster
|A group of concept codes making another idea. The Cluster may be made up of concepts from different ontologies or classifications e.g., Snomed CT and ICD10. Synonyms for Cluster are refset, value set, codelist, code set, grouper. These can also be called code sets. They are called Clusters to avoid confusion with medical classifications such as ICD 10
|[http://app.powerbi.com/view?r=eyJrIjoiZTY0ODY0YzEtMjhhYy00ZTViLWJjZDQtMWZjOWVkMTZlOGExIiwidCI6IjUwZjYwNzFmLWJiZmUtNDAxYS04ODAzLTY3Mzc0OGU2MjllMiIsImMiOjh9 Primary Care Domain Reference Set Portal]
|-
|Common Data Model
|This is the shared Data Model which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
It defines the transformation of data from multiple different databases with potentially multiple coding systems into a common storage format (data model) as well as a common representation (terminologies, vocabularies, coding schemes)
|[https://www.jamesserra.com/archive/2019/06/common-data-model/ Microsoft Example]
|-
|Concept
|A coded idea. For example, Name = Baker's Asthma, SNOMED CT code = 34015007
|
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|
|-
|Data Dictionary
|A centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format e.g. NHS Data Dictionary.
|[[wikipedia:Data_dictionary|Data Dictionary]]
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|[[wikipedia:Data_model|Data Model]]
[https://digital.nhs.uk/services/nhs-data-model-and-dictionary-service NHS Data Model and Dictionary Service]
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|
|-
|Data Warehouse
|A data warehouse stores data for the main purpose of reporting and data analysis. They are central repositories which bring together disparate data sets into one place, to enable reporting at scale and pace.
|[[wikipedia:Data_warehouse|Data Warehouse]]
|-
|DDS Information Model Manager
|The Information Model Manager is an application developed by the Endeavour Charitable Trust which provides a view of the Common Information Model and supports authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|[https://wiki.endeavourhealth.org/index.php?title=Discovery_health_information_model Discovery Health Information Model]
|-
|Endeavour Information Model
|
|
|-
|Expression (Snomed CT)
|A combination of concepts that represent something that is not already represented as a Concept or Cluster. It typically links things across concepts (disease of infection (finding), with bacteria name (organism), with sepsis (observable entity) with kidney (body structure).
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/SNOMED+CT+expression Snomed CT Expression]
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|
|-
|GitHub
|GitHub is a provider of version control and source-code management using [[wikipedia:Git|Git]] (which is software used for tracking changes in files). It enables software developers to safely store source code and work on it collaboratively, merging changes from different developers into the main "production" branch safely. It provides additional features such as bug/issue tracking, continuous integration, feature requests etc. GitHub was acquired by Microsoft in 2018.
|[[wikipedia:GitHub|GitHub]]
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|
|-
|HDRUK Phenotype Library
|This is a comprehensive, open access resource which exists to provide the research community with information, tools, and phenotyping algorithms for UK electronic health records (EHRs)
|[https://phenotypes.healthdatagateway.org/ HDR UK Phenotype Library]
|-
|Information Model
|A model describing ontologically standardised data items following a specific database schema within a specific relational database architecture.
|
|-
|Knowledge Graph
|An information architecture where concepts are represented as nodes and edges in a network of relationships. This is usually represented in a NoSQL architecture designed to around relationships between concepts.
|
|-
|[[Logical Data Model]]
|The logical data model (or logical schema) describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|[[wikipedia:Logical_schema|Logical Schema]]
|-
|Mauro Data Mapper
|Mauro Data Mapper is a third-party toolkit for the design and documentation of databases, data flows, and data standards, as well as related software artefacts such as data schemas and data forms. It was originally developed for the description of data in clinical research.
|[https://maurodatamapper.github.io/about/introduction/ Mauro Data Mapper]
|-
|Medical Classification
|Medical Classifications transform descriptions of procedures or diagnoses into standardised codes through the process of clinical coding. E.g., ICD10, OPCS4, LOINC. These
can also be called code sets. Hence, we call them a medical classification to avoid confusion with Clusters
|[[wikipedia:Medical_classification|Medical Classification]]
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|[https://digital.nhs.uk/services/terminology-servers NHSD Terminology Server]
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|[https://www.imohealth.com/ideas/article/data-normalization-working-toward-a-common-clinical-terminology/ Healthcare Normalisation]
[https://blog.clinicalarchitecture.com/what-is-data-normalization What is data normalisation?]
|-
|Normalisation (database)
|This is the process of structuring a relational database in accordance with a series of derived rules, called ''normal forms'', in order to reduce redundancy, duplication, and improve data integrity.
|[[wikipedia:Database_normalization|Normalisation]]
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|
|-
|OMOP Data Model
|OMOP stands for Observational Medical Outcomes Partnership, which was formed to inform the appropriate use of observational healthcare databases. OHDSI (Observational Health Data Sciences and Informatics) is a collaborative that now includes all of the original OMOP research investigators and will continue to develop tools using the OMOP common data model and vocabulary (OMOP is no longer and active programme).
|[https://www.ohdsi.org/data-standardization/the-common-data-model/ OMOP]
[https://github.com/OHDSI/CommonDataModel/blob/v5.4.0/inst/ddl/5.4/sql_server/OMOPCDM_sql_server_5.4_ddl.sql OMOP Common Data Model (GitHub)]
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. The most currently used ontology is SNOMED CT - the DDS ontology is made up of several ontologies (such as READ, CTV3 etc) mapped to SNOMED where appropriate SNOMED terms already exist, and extended where they don't.
A representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse (e.g., medicine). Example: Snomed CT.
|[[wikipedia:Ontology_(information_science)|Ontology]]
[https://termbrowser.nhs.uk/ NHSD SNOMED browser]

[https://www.snomed.org/ SNOMED.org]

[https://www.england.nhs.uk/digitaltechnology/digital-primary-care/snomed-ct/ SNOMED - NHS England]
|-
|OpenCodelists
|OpenCodelists was created by OpenSAFELY for creating and sharing codelists. See "Cluster" definition above.
|[https://www.opencodelists.org/ OpenCodelists]
[https://www.opensafely.org/ OpenSAFELY]
|-
|PESTLE Analysis
|A PESTLE analysis studies the key external factors (Political, Economic, Sociological, Technological, Legal and Environmental) that influence an organisation. It can be used in a range of different scenarios, and can guide people professionals and senior managers in strategic decision-making
|[https://www.cipd.co.uk/knowledge/strategy/organisational-development/pestle-analysis-factsheet Pestle Analysis]
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|[[wikipedia:Physical_schema|Physical Schema]]
|-
|Primary Care Reference Set
|This is a cluster of codes used within business rules authored and maintained by NHSD's primary care domain.
|[https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/quality-and-outcomes-framework-qof/quality-and-outcome-framework-qof-business-rules/primary-care-domain-reference-set-portal NHSD Primary Care Domain Reference Set Portal]
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SNOMED CT Concept
|A clinical idea to which a unique concept identifier has been assigned.
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/concept SNOMED CT Concept]
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|[[wikipedia:SQL|Structured Query Language]]
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|
|}

Glossary

2022-04-21T14:00:05Z

SimonMeredith: adding concepts/clusters

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary
{| class="wikitable sortable"
|+
!Term
!Definition
!External Links
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|[[wikipedia:Cloud_computing|Cloud Computing]]
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|[[wikipedia:Cloud_native_computing|Cloud Native Computing]]
|-
|Cluster
|A group of concept codes making another idea. The Cluster may be made up of concepts from different ontologies or classifications e.g., Snomed CT and ICD10. Synonyms for Cluster are refset, value set, codelist, code set, grouper. These can also be called code sets. They are called Clusters to avoid confusion with medical classifications such as ICD 10
|
|-
|Common Data Model
|This is the shared Data Model which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
It defines the transformation of data from multiple different databases with potentially multiple coding systems into a common storage format (data model) as well as a common representation (terminologies, vocabularies, coding schemes)
|[https://www.jamesserra.com/archive/2019/06/common-data-model/ Microsoft Example]
|-
|Concept
|A coded idea. For example, Name = Baker's Asthma, SNOMED CT code = 34015007
|
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|
|-
|Data Dictionary
|A centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format e.g. NHS Data Dictionary.
|[[wikipedia:Data_dictionary|Data Dictionary]]
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|[[wikipedia:Data_model|Data Model]]
[https://digital.nhs.uk/services/nhs-data-model-and-dictionary-service NHS Data Model and Dictionary Service]
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|
|-
|Data Warehouse
|A data warehouse stores data for the main purpose of reporting and data analysis. They are central repositories which bring together disparate data sets into one place, to enable reporting at scale and pace.
|[[wikipedia:Data_warehouse|Data Warehouse]]
|-
|DDS Information Model Manager
|The Information Model Manager is an application developed by the Endeavour Charitable Trust which provides a view of the Common Information Model and supports authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|[https://wiki.endeavourhealth.org/index.php?title=Discovery_health_information_model Discovery Health Information Model]
|-
|Endeavour Information Model
|
|
|-
|Expression
|A combination of concepts that represent something that is not already represented as a Concept or Cluster. It typically links things across concepts (disease of infection (finding), with bacteria name (organism), with sepsis (observable entity) with kidney (body structure).
|
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|
|-
|GitHub
|GitHub is a provider of version control and source-code management using [[wikipedia:Git|Git]] (which is software used for tracking changes in files). It enables software developers to safely store source code and work on it collaboratively, merging changes from different developers into the main "production" branch safely. It provides additional features such as bug/issue tracking, continuous integration, feature requests etc. GitHub was acquired by Microsoft in 2018.
|[[wikipedia:GitHub|GitHub]]
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|
|-
|HDRUK Phenotype Library
|This is a comprehensive, open access resource which exists to provide the research community with information, tools, and phenotyping algorithms for UK electronic health records (EHRs)
|[https://phenotypes.healthdatagateway.org/ HDR UK Phenotype Library]
|-
|Information Model
|A model describing ontologically standardised data items following a specific database schema within a specific relational database architecture.
|
|-
|Knowledge Graph
|An information architecture where concepts are represented as nodes and edges in a network of relationships. This is usually represented in a NoSQL architecture designed to around relationships between concepts.
|
|-
|[[Logical Data Model]]
|The logical data model (or logical schema) describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|[[wikipedia:Logical_schema|Logical Schema]]
|-
|Mauro Data Mapper
|Mauro Data Mapper is a third-party toolkit for the design and documentation of databases, data flows, and data standards, as well as related software artefacts such as data schemas and data forms. It was originally developed for the description of data in clinical research.
|[https://maurodatamapper.github.io/about/introduction/ Mauro Data Mapper]
|-
|Medical Classification
|Medical Classifications transform descriptions of procedures or diagnoses into standardised codes through the process of clinical coding. E.g., ICD10, OPCS4, LOINC. These
can also be called code sets. Hence, we call them a medical classification to avoid confusion with Clusters
|[[wikipedia:Medical_classification|Medical Classification]]
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|[https://digital.nhs.uk/services/terminology-servers NHSD Terminology Server]
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|
|-
|Normalisation (database)
|This is the process of structuring a relational database in accordance with a series of derived rules, called ''normal forms'', in order to reduce redundancy, duplication, and improve data integrity.
|[[wikipedia:Database_normalization|Normalisation]]
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|
|-
|OMOP Data Model
|OMOP stands for Observational Medical Outcomes Partnership, which was formed to inform the appropriate use of observational healthcare databases. OHDSI (Observational Health Data Sciences and Informatics) is a collaborative that now includes all of the original OMOP research investigators and will continue to develop tools using the OMOP common data model and vocabulary (OMOP is no longer and active programme).
|[https://www.ohdsi.org/data-standardization/the-common-data-model/ OMOP]
[https://github.com/OHDSI/CommonDataModel/blob/v5.4.0/inst/ddl/5.4/sql_server/OMOPCDM_sql_server_5.4_ddl.sql OMOP Common Data Model (GitHub)]
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. The most currently used ontology is SNOMED CT - the DDS ontology is made up of several ontologies (such as READ, CTV3 etc) mapped to SNOMED where appropriate SNOMED terms already exist, and extended where they don't.
A representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse (e.g., medicine). Example: Snomed CT.
|[[wikipedia:Ontology_(information_science)|Ontology]]
[https://termbrowser.nhs.uk/ NHSD SNOMED browser]

[https://www.snomed.org/ SNOMED.org]

[https://www.england.nhs.uk/digitaltechnology/digital-primary-care/snomed-ct/ SNOMED - NHS England]
|-
|OpenCodelists
|OpenCodelists was created by OpenSAFELY for creating and sharing codelists.
|[https://www.opencodelists.org/ OpenCodelists]
[https://www.opensafely.org/ OpenSAFELY]
|-
|PESTLE Analysis
|A PESTLE analysis studies the key external factors (Political, Economic, Sociological, Technological, Legal and Environmental) that influence an organisation. It can be used in a range of different scenarios, and can guide people professionals and senior managers in strategic decision-making
|[https://www.cipd.co.uk/knowledge/strategy/organisational-development/pestle-analysis-factsheet Pestle Analysis]
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|[[wikipedia:Physical_schema|Physical Schema]]
|-
|Primary Care Reference Set
|This is a cluster of codes used within business rules authored and maintained by NHSD's primary care domain.
|[https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/quality-and-outcomes-framework-qof/quality-and-outcome-framework-qof-business-rules/primary-care-domain-reference-set-portal NHSD Primary Care Domain Reference Set Portal]
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SNOMED CT Concept
|
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/concept SNOMED CT Concept]
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|[[wikipedia:SQL|Structured Query Language]]
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|
|}

Glossary

2022-04-21T13:50:41Z

SimonMeredith:

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary
{| class="wikitable sortable"
|+
!Term
!Definition
!External Links
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|[[wikipedia:Cloud_computing|Cloud Computing]]
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|[[wikipedia:Cloud_native_computing|Cloud Native Computing]]
|-
|Common Data Model
|This is the shared Data Model which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
|[https://www.jamesserra.com/archive/2019/06/common-data-model/ Microsoft Example]
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|[[wikipedia:Data_model|Data Model]]
[https://digital.nhs.uk/services/nhs-data-model-and-dictionary-service NHS Data Model and Dictionary Service]
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|
|-
|Data Warehouse
|A data warehouse stores data for the main purpose of reporting and data analysis. They are central repositories which bring together disparate data sets into one place, to enable reporting at scale and pace.
|[[wikipedia:Data_warehouse|Data Warehouse]]
|-
|DDS Information Model Manager
|The Information Model Manager is an application developed by the Endeavour Charitable Trust which provides a view of the Common Information Model and supports authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|[[Discovery Health Information Model]]
|-
|Endeavour Information Model
|
|
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|
|-
|GitHub
|GitHub is a provider of version control and source-code management using [[wikipedia:Git|Git]] (which is software used for tracking changes in files). It enables software developers to safely store source code and work on it collaboratively, merging changes from different developers into the main "production" branch safely. It provides additional features such as bug/issue tracking, continuous integration, feature requests etc. GitHub was acquired by Microsoft in 2018.
|[[wikipedia:GitHub|GitHub]]
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|
|-
|HDRUK Phenotype Library
|This is a comprehensive, open access resource which exists to provide the research community with information, tools, and phenotyping algorithms for UK electronic health records (EHRs)
|[https://phenotypes.healthdatagateway.org/ HDR UK Phenotype Library]
|-
|[[Logical Data Model]]
|The logical data model (or logical schema) describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|[[wikipedia:Logical_schema|Logical Schema]]
|-
|Mauro Data Mapper
|Mauro Data Mapper is a third-party toolkit for the design and documentation of databases, data flows, and data standards, as well as related software artefacts such as data schemas and data forms. It was originally developed for the description of data in clinical research.
|[https://maurodatamapper.github.io/about/introduction/ Mauro Data Mapper]
|-
|Medical Classification
|Medical Classifications transform descriptions of procedures or diagnoses into standardised codes through the process of clinical coding
|[[wikipedia:Medical_classification|Medical Classification]]
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|[https://digital.nhs.uk/services/terminology-servers NHSD Terminology Server]
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|
|-
|Normalisation (database)
|This is the process of structuring a relational database in accordance with a series of derived rules, called ''normal forms'', in order to reduce redundancy, duplication, and improve data integrity.
|[[wikipedia:Database_normalization|Normalisation]]
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|
|-
|OMOP Data Model
|OMOP stands for Observational Medical Outcomes Partnership, which was formed to inform the appropriate use of observational healthcare databases. OHDSI (Observational Health Data Sciences and Informatics) is a collaborative that now includes all of the original OMOP research investigators and will continue to develop tools using the OMOP common data model and vocabulary (OMOP is no longer and active programme).
|[https://www.ohdsi.org/data-standardization/the-common-data-model/ OMOP]
[https://github.com/OHDSI/CommonDataModel/blob/v5.4.0/inst/ddl/5.4/sql_server/OMOPCDM_sql_server_5.4_ddl.sql OMOP Common Data Model (GitHub)]
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. The most currently used ontology is SNOMED CT - the DDS ontology is made up of several ontologies (such as READ, CTV3 etc) mapped to SNOMED where appropriate SNOMED terms already exist, and extended where they don't.
|[[wikipedia:Ontology_(information_science)|Ontology]]
[https://termbrowser.nhs.uk/ NHSD SNOMED browser]

[https://www.snomed.org/ SNOMED.org]

[https://www.england.nhs.uk/digitaltechnology/digital-primary-care/snomed-ct/ SNOMED - NHS England]
|-
|OpenCodelists
|OpenCodelists was created by OpenSAFELY for creating and sharing codelists.
|[https://www.opencodelists.org/ OpenCodelists]
[https://www.opensafely.org/ OpenSAFELY]
|-
|PESTLE Analysis
|A PESTLE analysis studies the key external factors (Political, Economic, Sociological, Technological, Legal and Environmental) that influence an organisation. It can be used in a range of different scenarios, and can guide people professionals and senior managers in strategic decision-making
|[https://www.cipd.co.uk/knowledge/strategy/organisational-development/pestle-analysis-factsheet Pestle Analysis]
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|[[wikipedia:Physical_schema|Physical Schema]]
|-
|Primary Care Reference Set
|This is a cluster of codes used within business rules authored and maintained by NHSD's primary care domain.
|[https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/quality-and-outcomes-framework-qof/quality-and-outcome-framework-qof-business-rules/primary-care-domain-reference-set-portal NHSD Primary Care Domain Reference Set Portal]
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SNOMED CT Concept
|
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/concept SNOMED CT Concept]
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|
|}

Glossary

2022-04-21T13:47:24Z

SimonMeredith: more additions

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary
{| class="wikitable sortable"
|+
!Term
!Definition
!External Links
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|[[wikipedia:Cloud_computing|Cloud Computing]]
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|[[wikipedia:Cloud_native_computing|Cloud Native Computing]]
|-
|Common Data Model
|This is the shared Data Model which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
|[https://www.jamesserra.com/archive/2019/06/common-data-model/ Microsoft Example]
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|[[wikipedia:Data_model|Data Model]]
[https://digital.nhs.uk/services/nhs-data-model-and-dictionary-service NHS Data Model and Dictionary Service]
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|
|-
|Data Warehouse
|A data warehouse stores data for the main purpose of reporting and data analysis. They are central repositories which bring together disparate data sets into one place, to enable reporting at scale and pace.
|[[wikipedia:Data_warehouse|Data Warehouse]]
|-
|DDS Information Model Manager
|The Information Model Manager is an application developed by the Endeavour Charitable Trust which provides a view of the Common Information Model and supports authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|
|-
|Endeavour Information Model
|
|
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|
|-
|GitHub
|GitHub is a provider of version control and source-code management using [[wikipedia:Git|Git]] (which is software used for tracking changes in files). It enables software developers to safely store source code and work on it collaboratively, merging changes from different developers into the main "production" branch safely. It provides additional features such as bug/issue tracking, continuous integration, feature requests etc. GitHub was acquired by Microsoft in 2018.
|[[wikipedia:GitHub|GitHub]]
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|
|-
|HDRUK Phenotype Library
|This is a comprehensive, open access resource which exists to provide the research community with information, tools, and phenotyping algorithms for UK electronic health records (EHRs)
|[https://phenotypes.healthdatagateway.org/ HDR UK Phenotype Library]
|-
|[[Logical Data Model]]
|The logical data model (or logical schema) describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|[[wikipedia:Logical_schema|Logical Schema]]
|-
|Mauro Data Mapper
|Mauro Data Mapper is a third-party toolkit for the design and documentation of databases, data flows, and data standards, as well as related software artefacts such as data schemas and data forms. It was originally developed for the description of data in clinical research.
|[https://maurodatamapper.github.io/about/introduction/ Mauro Data Mapper]
|-
|Medical Classification
|Medical Classifications transform descriptions of procedures or diagnoses into standardised codes through the process of clinical coding
|[[wikipedia:Medical_classification|Medical Classification]]
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|[https://digital.nhs.uk/services/terminology-servers NHSD Terminology Server]
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|
|-
|Normalisation (database)
|This is the process of structuring a relational database in accordance with a series of derived rules, called ''normal forms'', in order to reduce redundancy, duplication, and improve data integrity.
|[[wikipedia:Database_normalization|Normalisation]]
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|
|-
|OMOP Data Model
|OMOP stands for Observational Medical Outcomes Partnership, which was formed to inform the appropriate use of observational healthcare databases. OHDSI (Observational Health Data Sciences and Informatics) is a collaborative that now includes all of the original OMOP research investigators and will continue to develop tools using the OMOP common data model and vocabulary (OMOP is no longer and active programme).
|[https://www.ohdsi.org/data-standardization/the-common-data-model/ OMOP]
[https://github.com/OHDSI/CommonDataModel/blob/v5.4.0/inst/ddl/5.4/sql_server/OMOPCDM_sql_server_5.4_ddl.sql OMOP Common Data Model (GitHub)]
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. The most currently used ontology is SNOMED CT - the DDS ontology is made up of several ontologies (such as READ, CTV3 etc) mapped to SNOMED where appropriate SNOMED terms already exist, and extended where they don't.
|[[wikipedia:Ontology_(information_science)|Ontology]]
[https://termbrowser.nhs.uk/ NHSD SNOMED browser]

[https://www.snomed.org/ SNOMED.org]

[https://www.england.nhs.uk/digitaltechnology/digital-primary-care/snomed-ct/ SNOMED - NHS England]
|-
|OpenCodelists
|OpenCodelists was created by OpenSAFELY for creating and sharing codelists.
|[https://www.opencodelists.org/ OpenCodelists]
[https://www.opensafely.org/ OpenSAFELY]
|-
|PESTLE Analysis
|A PESTLE analysis studies the key external factors (Political, Economic, Sociological, Technological, Legal and Environmental) that influence an organisation. It can be used in a range of different scenarios, and can guide people professionals and senior managers in strategic decision-making
|[https://www.cipd.co.uk/knowledge/strategy/organisational-development/pestle-analysis-factsheet Pestle Analysis]
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|[[wikipedia:Physical_schema|Physical Schema]]
|-
|Primary Care Reference Set
|This is a cluster of codes used within business rules authored and maintained by NHSD's primary care domain.
|[https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/quality-and-outcomes-framework-qof/quality-and-outcome-framework-qof-business-rules/primary-care-domain-reference-set-portal NHSD Primary Care Domain Reference Set Portal]
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SNOMED CT Concept
|
|[https://confluence.ihtsdotools.org/display/DOCGLOSS/concept SNOMED CT Concept]
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|
|}

Glossary

2022-04-21T13:20:41Z

SimonMeredith: adding more links

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary
{| class="wikitable sortable"
|+
!Term
!Definition
!External Links
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|[[wikipedia:Cloud_computing|Cloud Computing]]
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|[[wikipedia:Cloud_native_computing|Cloud Native Computing]]
|-
|Common Data Model
|This is the shared Data Model which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
|[https://www.jamesserra.com/archive/2019/06/common-data-model/ Microsoft Example]
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|[[wikipedia:Data_model|Data Model]]
[https://digital.nhs.uk/services/nhs-data-model-and-dictionary-service NHS Data Model and Dictionary Service]
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|
|-
|Data Warehouse
|A data warehouse stores data for the main purpose of reporting and data analysis. They are central repositories which bring together disparate data sets into one place, to enable reporting at scale and pace.
|[[wikipedia:Data_warehouse|Data Warehouse]]
|-
|DDS Information Model Manager
|The Information Model Manager is an application developed by the Endeavour Charitable Trust which provides a view of the Common Information Model and supports authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|
|-
|Endeavour Information Model
|
|
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|
|-
|[[Logical Data Model]]
|The logical data model (or logical schema) describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|[[wikipedia:Logical_schema|Logical Schema]]
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|[https://digital.nhs.uk/services/terminology-servers NHSD Terminology Server]
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|
|-
|Normalisation (database)
|This is the process of structuring a relational database in accordance with a series of derived rules, called ''normal forms'', in order to reduce redundancy, duplication, and improve data integrity.
|[[wikipedia:Database_normalization|Normalisation]]
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|
|-
|OMOP Data Model
|OMOP stands for Observational Medical Outcomes Partnership, which was formed to inform the appropriate use of observational healthcare databases. OHDSI (Observational Health Data Sciences and Informatics) is a collaborative that now includes all of the original OMOP research investigators and will continue to develop tools using the OMOP common data model and vocabulary (OMOP is no longer and active programme).
|[https://www.ohdsi.org/data-standardization/the-common-data-model/ OMOP]
[https://github.com/OHDSI/CommonDataModel/blob/v5.4.0/inst/ddl/5.4/sql_server/OMOPCDM_sql_server_5.4_ddl.sql OMOP Common Data Model (GitHub)]
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. The most currently used ontology is SNOMED CT - the DDS ontology is made up of several ontologies (such as READ, CTV3 etc) mapped to SNOMED where appropriate SNOMED terms already exist, and extended where they don't.
|[[wikipedia:Ontology_(information_science)|Ontology]]
[https://termbrowser.nhs.uk/ NHSD SNOMED browser]

[https://www.snomed.org/ SNOMED.org]

[https://www.england.nhs.uk/digitaltechnology/digital-primary-care/snomed-ct/ SNOMED - NHS England]
|-
|PESTLE Analysis
|A PESTLE analysis studies the key external factors (Political, Economic, Sociological, Technological, Legal and Environmental) that influence an organisation. It can be used in a range of different scenarios, and can guide people professionals and senior managers in strategic decision-making
|[https://www.cipd.co.uk/knowledge/strategy/organisational-development/pestle-analysis-factsheet Pestle Analysis]
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|[[wikipedia:Physical_schema|Physical Schema]]
|-
|Primary Care Reference Set
|This is a cluster of codes used within business rules authored and maintained by NHSD's primary care domain.
|[https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/quality-and-outcomes-framework-qof/quality-and-outcome-framework-qof-business-rules/primary-care-domain-reference-set-portal NHSD Primary Care Domain Reference Set Portal]
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|
|}

Glossary

2022-04-21T12:52:39Z

SimonMeredith: Adding terms and links

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary
{| class="wikitable sortable"
|+
!Term
!Definition
!Links
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|[[wikipedia:Cloud_computing|Cloud Computing]]
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|[[wikipedia:Cloud_native_computing|Cloud Native Computing]]
|-
|Common Data Model
|This is the Data Model which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
|
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|
|-
|DDS Information Model Manager
|The Information Model Manager is an application developed by the Endeavour Charitable Trust which provides a view of the Common Information Model and supports authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|
|-
|Endeavour Information Model
|
|
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|
|-
|[[Logical Data Model]]
|The logical data model describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|
|-
|Normalisation (database)
|This is the process of structuring a relational database in accordance with a series of derived rules, called ''normal forms'', in order to reduce redundancy, duplication, and improve data integrity.
|[[wikipedia:Database_normalization|Normalisation]]
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. The most currently used ontology is SNOMED CT - the DDS ontology is made up of several ontologies (such as READ, CTV3 etc) mapped to SNOMED where appropriate SNOMED terms already exist, and extended where they don't.
|[[wikipedia:Ontology_(information_science)|Ontology]]
[https://termbrowser.nhs.uk/ NHSD SNOMED browser]

[https://www.snomed.org/ SNOMED.org]

[https://www.england.nhs.uk/digitaltechnology/digital-primary-care/snomed-ct/ SNOMED - NHS England]
|-
|PESTLE Analysis
|A PESTLE analysis studies the key external factors (Political, Economic, Sociological, Technological, Legal and Environmental) that influence an organisation. It can be used in a range of different scenarios, and can guide people professionals and senior managers in strategic decision-making
|[https://www.cipd.co.uk/knowledge/strategy/organisational-development/pestle-analysis-factsheet Pestle Analysis]
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|
|}

Glossary

2022-04-21T12:34:32Z

SimonMeredith: added Common Data Model

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary
{| class="wikitable sortable"
|+
!Term
!Definition
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|-
|Common Data Model
|This is the [[Data Model]] which has been adopted as a standard for an organisation or group. It defines how the elements of data relate to one another and enables data to be transferred between different systems which share the Common Data Model with ease.
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|-
|DDS Information Model Manager
|The Information Model Manager is an application within DDS which provides a view of the Common Information Model
and to support authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|-
|Endeavour Information Model
|
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|-
|[[Logical Data Model]]
|The logical data model describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|-
|Normalisation (database)
|
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. NEED SOME SORT OF REAL-LIFE EXAMPLE
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|}

Glossary

2022-04-19T07:54:08Z

SimonMeredith: added link

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary
{| class="wikitable sortable"
|+
!Term
!Definition
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|-
|DDS Information Model Manager
|The Information Model Manager is an application within DDS which provides a view of the Common Information Model
and to support authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|-
|Endeavour Information Model
|
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|-
|[[Logical Data Model]]
|The logical data model describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|-
|Normalisation (database)
|
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. NEED SOME SORT OF REAL-LIFE EXAMPLE
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|}

Logical Data Model

2022-04-19T07:52:47Z

SimonMeredith: first detail page - for demo purposes. Having problems with images

The logical data model describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)

== Simplified Definition ==
A Logical Data Model shows the relationships between different items of data. For example, for a simple online stationery shop, a Logical Data Model might show that a c''ustomer'' is one data item, and the ''customer'' can be related to a number of ''products'' in the online shop via the ''customer orders''. This might be represented in the following simple Logical Data Model:

Each customer order has one customer, and one or more products. It can be further illustrated:

== Useful links ==
<nowiki>https://www.gooddata.com/blog/how-build-logical-data-models-scale-analytical-applications/</nowiki>

<nowiki>https://en.wikipedia.org/wiki/Data_modeling</nowiki>

== Diagrams ==

Glossary

2022-04-19T07:34:31Z

SimonMeredith: table formatting

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary
{| class="wikitable sortable"
|+
!Term
!Definition
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|-
|DDS Information Model Manager
|The Information Model Manager is an application within DDS which provides a view of the Common Information Model
and to support authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|-
|Endeavour Information Model
|
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|-
|Logical Data Model
|The logical data model describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|-
|Normalisation (database)
|
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. NEED SOME SORT OF REAL-LIFE EXAMPLE
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|}

Glossary

2022-04-19T07:33:48Z

SimonMeredith: Initial draft of glossary

This is an area of the Wiki for members of the Discovery Collaborative to share definitions of common terms. It is public and can be referenced from other documents and websites as necessary
{| class="wikitable sortable mw-collapsible mw-collapsed"
|+
!Term
!Definition
|-
|Cloud
|The Cloud refers to IT infrastructure, platforms, and services that are hosted in a remote data centre, managed by a third party. Typically these data centres are run and managed by large companies such as Microsoft (Azure), Amazon (AWS), and Google (GWP), but there are many other providers too. Using the cloud removes the need for customers to buy and maintain their own physical IT equipment. Instead, the cloud provider typically charges customers daily rates based on storage used, computational power used, memory, number of servers etc
|-
|Cloud Native
|Cloud Native refers to building software and systems in the cloud that make use of services made available by cloud providers, to run scalable, resilient applications. For example, instead of creating a SQL database on a server in the cloud, which the customer would need to monitor, maintain, patch, back-up etc, they could make use of a “SQL database as a service” from the cloud provider, who would automatically take care of all the maintenance without any further intervention from the customer
|-
|Conceptual Data Model
|This is a high level model, describing business processes and how they relate to one another. In simple terms, it is a map of concepts and rules relating to the business which can then be used to define relationships between data entities in order to describe the business concepts – this leads to the creation of a logical data model
|-
|Data Model
|Data models exist at different levels of abstraction, from conceptual data models which are high-level and consider business activity and relationships, to a physical data model which represents the implementation of a database. A data model shows how elements of data relate to one another and how they are structured, how they relate to business needs, and how they can be manipulated.
|-
|Data Platform
|A Data Platform is any digital solution which enables an organisation to store, transform, aggregate, and analyse data. It integrates many different technologies for different purposes to meet the data needs of the organisation. It includes security and access controls to ensure IG compliance
|-
|Database Index
|Indexes are used to quickly sort and retrieve data in a database. They may take the form of a physical, clustered index, which is the physical ordering of the data in the table (e.g. by ID or surname), or a non-clustered index which describes the logical ordering of the data separately – like the index at the back of a book: the index key is ordered (e.g. surname) and next to it is information showing where to find the record.
|-
|Database Schema
|This is the definition of the structure of a relational database and defines how data is organised, including the table names, fields (also known as columns), data types, constraints, indexes, and relationships between these entities. The schema is a blueprint of how the database is constructed.
|-
|DDS Information Model Manager
|The Information Model Manager is an application within DDS which provides a view of the Common Information Model
and to support authoring of the content of the model. It can be used to view the model structure and content, download artefacts (e.g. value sets), and manage the ontology. It can be used to view entities and their relationships (e.g. an A&E admission, which is an Encounter and has a subject of a Patient and has a sub-encounter of a Triage Encounter etc)
|-
|Endeavour Information Model
|
|-
|FHIR
|FHIR stands for Fast Healthcare Interoperability Resources. It is an international standard for health care data exchange and is published by HL7. It is designed to enable the exchange of healthcare related information, including clinical, administrative, publich-health, and research data. DDS stores data in the core in FHIR format before it is restructured and sent to subscriber databases
|-
|Graph Database
|Graph Databases are used to represent data as elements and their relationships (known as nodes and
edges). They are particularly beneficial when representing large datasets with complex and numerous relationships, such as social media contacts and relations. Unlike a relational database, where to return data it is often necessary to join many tables, in a graph database this isn’t necessary. Relationships are stored natively alongside the data elements enabling much faster querying
|-
|Logical Data Model
|The logical data model describes how data objects relate to each other, but is independent of the technology or database management system used. Logical data models typically show entities (e.g. a patient, an appointment, a result), relationships (e.g. an appointment is linked to a patient via a unique identifier), and attributes (information that is useful to further describe the entities)
|-
|NHS Terminology Server
|The NHS Terminology Server “is a FHIR compliant solution that holds and disseminates assured international terminologies and classifications (such as SNOMED-CT and ICD-10) and national terminologies (such as NHS Data Model and Dictionary codes).”
See <nowiki>https://digital.nhs.uk/services/terminology-servers</nowiki>. Content is delivered in machine readable format and can be accessed as a real-time resource through APIs to support other applications.
|-
|Normalisation (health data)
|In healthcare, this is the process of taking data from different sources, in different formats and with different code sets, and converting the data into a singular, unified clinical language or terminology – for example mapping proprietary codes from EPR systems to SNOMED codes
|-
|Normalisation (database)
|
|-
|NoSQL Database
|NoSQL stands for Not Only SQL, and is a generic name given to databases which store data in a non-tabular format. They are not relational databases – they may use storage techniques such as key-value pairs, document stores, graph databases. In some cases, SQL can still be used to query them
|-
|On-Premise (On-Prem)
|IT infrastructure build and maintained in-house – for example, database servers sitting in the basement of a hospital building which the IT department maintain and upgrade themselves
|-
|Ontology
|An Ontology is a set of concepts and categories showing the relationships and properties between them in a particular domain. In healthcare, and ontology is used for modelling the semantics of medical concepts and to enable the exchange of medical data between systems. NEED SOME SORT OF REAL-LIFE EXAMPLE
|-
|Physical Data Model
|The physical data model describes how a database should be structured and is a representation of table structures, columns, column names, column constraints, primary keys, foreign keys, and any other physical features of the database. A database is an implementation of a physical data model.
|-
|Relational Database
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|-
|SQL
|A Relational database is one where the data is stored in tables, featuring rows and columns and has predefined relationships between the data items. Typically, a table will have a primary key, which is a unique identifier for items in that table. Other tables will reference that primary key for items that are related to it by means of a foreign key. For example, in a table of patients, the primary key might be the NHS number
|-
|SQL Database
|A SQL Database is a relational database, which is a collection of tables storing a specific set of structured data, with a fixed schema, which can be queried using SQL
|}

Current published data

2022-03-02T08:00:24Z

SimonMeredith: Renamed table column heading - was incorrectly titled "mental health". Changed to "Community Care" as requested by Steve Janering

This article provides the latest data publisher information by sector, geographical area, service, and system provider.

'''Total number of distinct person records in the Discovery Data Service = 14,413,799'''

=== GP practices ===
Total number of distinct GP patient records = 16,957,835

* [[#NEL|North East London]] = 5,761,872
*[[#NWL|North West London]] = 6,233,648
* [[#SEL|South East London]] = 4,931,129
* Other = 31,186
{{Note|10 years' of 'left and deads' data is included for inner North East London - City & Hackney, Newham, Tower Hamlets, and Waltham Forest.}}

Total number of GP practices = 850

*[[#NEL|North East London]] = 293
*[[#NWL|North West London]] = 351
*[[#SEL|South East London]] = 204
* Other = 2
{{Note| This also includes closed GP practices.}}

=== Acute services ===

* Total number of acute patient records = 5,063,878
*[[Category:Data Sets]] Total number of acute services = 4

=== Community and Mental Health services ===

*Total number of Community and Mental Health patient records = 6,490,752
*Total number of Community and Mental Health services = 108

=== Other services ===

*Total number of other patient records = 3,585,020
*Total number of other services = 22

===North East London ===
<table class="borders">
<tr>
<th colspan="3" class="borders" style="border-bottom: solid 1px black">Primary Care GP</th>
<th colspan="2" class="borders" style="border-bottom: solid 1px black">Secondary Care</th>
<th class="borders" style="border-bottom: solid 1px black">Unscheduled Care</th>
<th colspan="2" class="borders" style="border-bottom: solid 1px black">Community & Mental Health</th>
</tr>
<tr>
<td class="borders" style="background-color: #ff9800;color: #ffffff;font-weight: bold;">EMIS Web</td>
<td class="borders" style="background-color: #ff9800;color: #ffffff;font-weight: bold;">SystmOne</td>
<td class="borders" style="background-color: #ff9800;color: #ffffff;font-weight: bold;">Vision</td>
<td class="borders" style="background-color: #37474f;color: #ffffff;font-weight: bold;">Cerner Millenium</td>
<td class="borders" style="background-color: #37474f;color: #ffffff;font-weight: bold;">Medway</td>
<td class="borders" style="background-color: #ff9800;color: #ffffff;font-weight: bold;">Adastra</td>
<td class="borders" style="background-color: #37474f;color: #ffffff;font-weight: bold" ; "border-bottom: solid 1px black";>EMIS Web Community</td>
<td class="borders" style="background-color: #37474f;color: #ffffff;font-weight: bold;" "border-bottom: solid 1px black";>Rio</td>
</tr>
<tr>
<td class="borders">Barking & Dagenham 32</td>
<td class="borders">Barking & Dagenham 1</td>
<td class="borders">Havering 1</td>
<td class="borders"><ref>ADT feed only.</ref>Homerton Hospital Trust</td>
<td class="borders">BHRUT</td>
<td class="borders">CHUHSE</td>
<td class="borders">Barts Community Services
9
</td>
<td class="borders">East London Foundation Trust*</td>
</tr>
<tr>
<td class="borders">City & Hackney 40</td>
<td class="borders" style="border-bottom: solid 1px black">Redbridge 5</td>
<td class="noborders"></td>
<td class="borders"><ref>ADT feed plus daily file feed that includes CDE and CDS files.</ref>St Barts Hospital Trust</td>
<td class="noborders"></td>
<td class="borders">Hurley Group</td>
<td class="borders">City & Hackney GP Confederation</td>
<td class="borders">North East London Foundation Trust*</td>
</tr>
<tr>
<td class="borders">Havering 39 1*</td>
<td class="borders">Waltham Forest 5</td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="borders">London Ambulance Service</td>
<td class="borders">Patient First</td>
<td class="noborders"></td>
</tr>
<tr>
<td class="borders">Newham 46</td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="borders">Newham OOH*</td>
<td class="noborders"></td>
<td class="noborders"></td>
</tr>
<tr>
<td class="borders">Redbridge 37</td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="borders">PELC</td>
<td class="noborders"></td>
<td class="noborders"></td>
</tr>
<tr>
<td class="borders">Tower Hamlets 32</td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
</tr>
<tr>
<td class="borders" style="border-bottom: solid 1px black">Waltham Forest 34</td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
</tr>

</table>

===North West London ===
<table class="borders">
<tr>
<th colspan="2" class="borders" style="border-bottom: solid 1px black">Primary Care GP</th>
<th class="borders" style="border-bottom: solid 1px black">Secondary Care</th>
<th colspan="2" class="borders" style="border-bottom: solid 1px black">Community Care</th>
<th colspan="3"></th>
</tr>
<tr>
<td class="borders" style="background-color: #ff9800;color: #ffffff;font-weight: bold;">EMIS Web</td>
<td class="borders" style="background-color: #ff9800;color: #ffffff;font-weight: bold;">SystmOne</td>
<td class="borders" style="background-color: #37474f;color: #ffffff;font-weight: bold;">Cerner Millenium</td>
<td class="borders" style="background-color: #37474f;color: #ffffff;font-weight: bold;">SystmOne Community</td>
<td class="borders" style="background-color: #37474f;color: #ffffff;font-weight: bold;">EMIS Web Community</td>
</tr>

<tr>
<td class="borders">Brent 51</td>
<td class="borders">Central London 33 1*</td>
<td class="borders" style="border-bottom: solid 1px black"><ref>ADT feed only.</ref>Imperial College Healthcare NHS Trust
<td class="borders">Central London Community Healthcare
40
</td>
<td class="borders">The Confederation -
Hillingdon CIC
</td>
</tr>

<tr>
<td class="borders" style="border-bottom: solid 1px black">Ealing 1</td>
<td class="borders">Ealing 73 2*</td>
<td class="noborders"></td>
<td class="borders">Central & North West London NHS Trust
27
</td>
<td class="borders">Harrow Covid / GP Access Hub</td>
</tr>

<tr>
<td class="borders">Harrow 32</td>
<td class="borders">Hammersmith & Fulham 28</td>
<td class="noborders"></td>
<td class="borders">Chiswick PCN Extended Hours Hub</td>
<td class="borders">K&W Healthcare</td>
</tr>
<tr>
<td class="borders">Hillingdon 43 2*</td>
<td class="borders">Hounslow 44 1*</td>
<td class="noborders"></td>
<td class="borders">Ealing GP Access Centres</td>
<td class="noborders"></td>
</tr>
<tr>
<td class="borders" style="border-bottom: solid 1px black">Hounslow 1</td>
<td class="borders" style="border-bottom: solid 1px black">West London 41</td>
<td class="noborders"></td>
<td class="borders" style="border-bottom: solid 1px black">Hammersmitth & Fulham Weekend Plus</td>
<td class="noborders"></td>
</tr>
<tr>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="borders" style="border-bottom: solid 1px black">Hounslow Extended Hours 4</td>
<td class="noborders"></td>
</tr>

<tr>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="borders" style="border-bottom: solid 1px black">Imperial College Healthcare NHS Trust

10
</td>
<td class="noborders"></td>
</tr>

<tr>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="borders">West London Extended Hours</td>
<td class="noborders"></td>
</tr>

<tr>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="noborders"></td>
<td class="borders">West London NHS Trust 21 5*</td>
<td class="noborders"></td>
</tr><tr><td></td><td></td><td></td><td></td><td></td></tr></table>

===South East London ===

<table class="borders">
<tr>
<th colspan="2" class="borders" style="border-bottom: solid 1px black">Primary Care GP</th>
<th class="borders" style="border-bottom: solid 1px black">Unscheduled Care</th>
<th colspan="5"></th>
</tr>
<tr>
<td class="borders" style="background-color: #ff9800;color: #ffffff;font-weight: bold;">EMIS Web</td>
<td class="borders" style="background-color: #ff9800;color: #ffffff;font-weight: bold;">Vision</td>
<td class="borders" style="background-color: #ff9800;color: #ffffff;font-weight: bold;">Adastra</td></tr>
<tr>
<td class="borders">Bexley 19</td>

<td class="borders">Bexley 2</td>
<td class="borders" style="border-bottom: solid 1px black">London Ambulance Service</td>
</tr>
<tr>

<td class="borders">Bromley 43
</td>
<td class="noborders"></td>
<td class="noborders"></td>
</tr>
<tr>
<td class="borders" style="border-bottom: solid 1px black">Greenwich
19 12*
</td>
<td class="noborders"></td>
<td class="noborders"></td>
</tr>
<tr>
<td class="borders" style="border-bottom: solid 1px black">Lambeth 40</td>
<td class="noborders"></td>
<td class="noborders"></td>
</tr>
<tr>
<td class="borders" style="border-bottom: solid 1px black">Lewisham 31</td>
<td class="noborders"></td>
<td class="noborders"></td>
</tr>
<tr>
<td class="borders" style="border-bottom: solid 1px black">Southwark 32</td>
<td class="noborders"></td>
<td class="noborders"></td>
</tr>
</table>

Numbers indicate the number of GP practices that are sending live data.
Numbers and text in red * indicate data that is not yet live.
{{Note| This does not include closed GP practices.}}

'''Last update:''' 1 March 2022

'''See also:'''

*[[Current data sets]]
*[[Latency of extract data feeds]]
<references />