Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records

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Objectives Electronic health record (EHR) data are prone to missingness and errors. Previously, we devised an enriched chart review protocol where a “roadmap” of auxiliary diagnoses was used to recover missing values. Still, chart reviews are expensive and time-intensive, limiting the number of patients whose data can be reviewed. Now, we investigate the accuracy and scalability of a…

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