  Electronic health records (EHRs) contain vast amounts of complex data, but
harmonizing and processing this information remains a challenging and costly
task requiring significant clinical expertise. While large language models
(LLMs) have shown promise in various healthcare applications, their potential
for abstracting medical concepts from EHRs remains largely unexplored. We
introduce EHRmonize, a framework leveraging LLMs to abstract medical concepts
from EHR data. Our study uses medication data from two real-world EHR databases
to evaluate five LLMs on two free-text extraction and six binary classification
tasks across various prompting strategies. GPT-4o's with 10-shot prompting
achieved the highest performance in all tasks, accompanied by Claude-3.5-Sonnet
in a subset of tasks. GPT-4o achieved an accuracy of 97% in identifying generic
route names, 82% for generic drug names, and 100% in performing binary
classification of antibiotics. While EHRmonize significantly enhances
efficiency, reducing annotation time by an estimated 60%, we emphasize that
clinician oversight remains essential. Our framework, available as a Python
package, offers a promising tool to assist clinicians in EHR data abstraction,
potentially accelerating healthcare research and improving data harmonization
processes.
