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These articles describe the Endeavour information modelling technologies and services.  
These articles describe the Endeavour information modelling technologies and services.  

Revision as of 08:30, 11 March 2023


These articles describe the Endeavour information modelling technologies and services.

These technologies form part of a broader set of Endeavour open source technologies, many of which have been adapted and used by a large scale NHS London Data linkage and normalisation Service covering a population of 7 million registered citizens.

Health Information model

If health related data is brought together at the level of the individual in close to real time, and stored together at the level of a medium size residential population, and made available via an ontology, and then subsequently used for individual and population based decision support, great benefits to health can accrue.

To make sense of huge variation with thousands of data types and millions of codes from thousands of providers using scores of different systems, it is useful to create an information model covering a data model, an ontology of concepts bound to the data model, and presented via an application in a way that enables query of all of the data by a none technical user.

Having established such a model , it is then possible to construct logical definitions of query and concept sets that can then be used on the data published from the sources.

Services that link and normalise the data can use the model and/ or the ontologies within it, creating maps between source data and the common model.

This articles and linked pages describe a new kind of information model, one that takes account of the convergence of graph stores, relational databases , and document stores.

Most models in healthcare either use bespoke health care languages such as those used by HL7 or openEHR, or conventional entity diagrams with a separate terminology server. The approach used in the Endeavour information model is to adopt and adapt the Main stream semantic web languages, based on a view of health data as a graph with the nodes and edges modelled as RDF IRIs.

The model is not a new standard or an invention of new concepts. Instead, the content of the Endeavour IM incorporates concepts from a number of recognised sources including:

a) The main stream health ontology Snomed-CT with extensions to accommodate the unmapped NHS data dictionary attributes, local codes, and code taxonomies such as OPCS, ICD10 as well as the legacy mappings to Read 2.

b) The main stream messaging model resources such as FHIR making the IM FHIR compatible via simple transforms.

c) The main stream query definitions such as QOF rules and dataset definitions.

Information model Components

Health Information model

An overview of the approach to the Health information model, the purpose, and type of content.

Information model languages

The Semantic Web languages used to build the various components of the information models

Health query definition

A logical machine readable definition of query, covering the majority of health data query requirements.

Information model meta model

The class model (shapes model) of the classes used to hold the model content.

Mapping concepts and transforming published data

Introduces the approaches to matching and mapping concepts and the structural maps used in transforming published data.

Architectures -

A high level overview of the architectures that the technologies contribute to

GitHub repositories

Descriptions and information relating to the application source code, .

Applications and APIs

These articles provide information about the applications that have been by the technologies

  • Data Sharing manager- DSM provides a visual representation of data that is being shared and processed and by which organisations, regions, and/or services.
  • ASSIGN- UPRN address matching application - a web based application that matches single or batches of hand entered address to authoritative addresses and assigns a unique property reference number.
  • Alert Generator - APIs that generates a patient alert based on a query, sends a notification to recipients and provides recipients with access to a web application to view the content of the record according to the data set
  • Data Distribution services that distribute daily weekly or adhoc small data sets from the linked core data stores with examples from 2020