Machine learning optimization of an electronic health record audit for heart failure in primary care.

ABSTRACT:

Aims

The diagnosis of heart failure (HF) is an important problem in primary care. We previously demonstrated a 74% increase in registered HF diagnoses in primary care electronic health records (EHRs) following an extended audit procedure. What remains unclear is the accuracy of registered HF pre-audit and which EHR variables are most important in the extended audit strategy. This study aims to describe the diagnostic HF classification sequence at different stages, assess general practitioner (GP) HF misclassification, and test the predictive performance of an optimized audit.

Methods and results

This is a secondary analysis of the OSCAR-HF study, a prospective observational trial including 51 participating GPs. OSCAR used an extended audit based on typical HF risk factors, s

SUBMITTER: Raat W 

PROVIDER: S-EPMC8787980 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature