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Evaluating openEHR for defining machine readable electronic health record phenotypes: lessons from the CALIBER resource

Papez, V; Denaxas, S; (2017) Evaluating openEHR for defining machine readable electronic health record phenotypes: lessons from the CALIBER resource. Presented at: Informatics for Health 2017, Manchester.

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Abstract

Linked Electronic Health Records (EHR) are increasingly being used for research. The process of defining and validating EHR phenotypes poses significant challenges as data have been collected for care, auditing or administrative purposes and not research. Challenges are further amplified by the lack of common standards for describing EHR phenotypes, making their reproducibility problematic. While phenotype components are often controlled clinical terminology terms, definitions and algorithmic logic are expressed in unstructured textual and/or graphical form which is not machine-readable. Open-source clinical data specifications, such as openEHR, can potentially address some of these challenges and used to define computable EHR-derived phenotypes. The semantic interoperability between openEHR archetypes is based on a binding mechanism between internal archetype codes and controlled clinical terminologies. Using type- 2 diabetes as a case-study, the aim of our research was to evaluate openEHR for defining EHR-derived phenotypes in a large-scale linked EHR resource in the UK.

Type: Conference item (Presentation)
Title: Evaluating openEHR for defining machine readable electronic health record phenotypes: lessons from the CALIBER resource
Event: Informatics for Health 2017
Location: Manchester
Dates: 24 April 2017 - 26 April 2017
Publisher version: http://informaticsforhealth.org/
Keywords: Clinical research informatics
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Pop Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Pop Health Sciences > Institute of Health Informatics > Clinical Epidemiology
URI: http://discovery.ucl.ac.uk/id/eprint/1555295
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