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Feasibility of using EN 13606 clinical archetypes for defining computable phenotypes

Research output: Chapter in Book/Report/Conference proceedingConference paper

Original languageEnglish
Title of host publicationDigital Personalized Health and Medicine - Proceedings of MIE 2020
EditorsLouise B. Pape-Haugaard, Christian Lovis, Inge Cort Madsen, Patrick Weber, Per Hostrup Nielsen, Philip Scott
PublisherIOS Press
Pages228-232
Number of pages5
ISBN (Electronic)9781643680828
DOIs
Published16 Jun 2020
Event30th Medical Informatics Europe Conference, MIE 2020 - Geneva, Switzerland
Duration: 28 Apr 20201 May 2020

Publication series

NameStudies in Health Technology and Informatics
Volume270
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference30th Medical Informatics Europe Conference, MIE 2020
CountrySwitzerland
CityGeneva
Period28/04/20201/05/2020

King's Authors

Abstract

Introduction: Computable phenotypes are gaining importance as structured and reproducible method of using electronic health data to identify people with certain clinical conditions. A formal standard is not available for defining and formally representing phenotyping algorithms. In this paper, we have tried to build a formal representation of such phenotyping algorithm. Methods: We built EN 13606 EHR standard for building clinical archetypes to represent the computable phenotyping algorithm for 'diagnosis of cardiac failure'. As part of this work, we created a set of new clinical archetypes for defining 'cardiac failure diagnosis'. The EN13606 editor called Object Dictionary Client was used which was in-house developed by University College London. We evaluated the ability of EN 13606 to provide clinical archetypes to define EHR phenotyping algorithms using the predefined desiderata for the purpose [Mo et al]. Results: EN 13606 archetypes could represent phenotype components grouped and nested based on their logical meaning. It was possible to build the EHR phenotyping algorithm with the clinical elements and their interrelationships along with hierarchical structure and temporal criteria. But the specific mathematical calculation and temporal relations involved in the algorithm was difficult to incorporate. These will need to be coded and integrated within the clinical information system. These archetypes can be mapped for comparison with the openEHR models. Binding to external clinical terminology is fully supported. However, it does not satisfy all the desiderata defined by Mo et al. A possible way could be an approach using phenotype ontologies and its architectural representation integrated with ISO interoperability. Conclusion: The EN13606 archetypes can be used to define the phenotype algorithm that basically identifies patients by a set of clinical characteristics in their records. Phenotype representations defined in EN 13606 do not satisfy all the desiderata proposed by Mo et al. and thus currently has a limited ability to define the computable phenotyping algorithms. Further work is required to make the EN13606 standard to fully support the objective.

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