Defining phenotypes of disease severity for long-term cardiovascular, renal, metabolic, and mental health conditions in primary care electronic health records: A mixed-methods study using the nominal group technique

We developed a methodology for identifying severity phenotypes in EHRs. Severity phenotypes were identified for diabetes (type 1 and 2), ischaemic heart disease, CKD and peripheral vascular disease. Data quality in EHR should be improved for under-recorded severity measures.

Authors

Shamil Haroon (ORCID: https://orcid.org/0000-0002-0096-1413)
Krishnarajah Nirantharakumar (ORCID: https://orcid.org/0000-0002-6816-1279)
Thomas Jackson (ORCID: https://orcid.org/0000-0001-6320-9600)
Jennifer N. Cooper (ORCID: https://orcid.org/0000-0001-7590-7166)
Leah Fitzsimmons (ORCID: https://orcid.org/0000-0002-7401-0186)
Francesca L. Crowe (ORCID: https://orcid.org/0000-0003-4026-1726)
Eleanor Hathaway

Institutions

University of Birmingham (GB)

Publication Details

Journal: Journal of Biomedical Informatics
Published: 2025-04-21
DOI: https://doi.org/10.1016/j.jbi.2025.104831
Primary Topic: Chronic Disease Management Strategies
Type: article
Field-Weighted Citation Impact: 0.00

Defining phenotypes of disease severity for long-term cardiovascular, renal, metabolic, and mental health conditions in primary care electronic health records: A mixed-methods study using the nominal group technique

Authors

Institutions

Publication Details

Defining phenotypes of disease severity for long-term cardiovascular, renal, metabolic, and mental health conditions in primary care electronic health records: A mixed-methods study using the nominal group technique

Defining phenotypes of disease severity for long-term cardiovascular, renal, metabolic, and mental health conditions in primary care electronic health records: A mixed-methods study using the nominal group technique

Abstract

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