Evaluating GPT's Capability in Identifying Stages of Cognitive Impairment from Electronic Health Data
This study addresses the problem of timely and accurate identification of cognitive impairment for clinicians and researchers, providing an incremental solution by leveraging existing GPT-4o technology.
The study tackled the problem of identifying stages of cognitive impairment from electronic health data, achieving a weighted kappa score of 0.83 in determining the global Clinical Dementia Rating and 0.91 in differentiating between normal cognition, mild cognitive impairment, and dementia. The results were based on 769 patients from Massachusetts General Hospital and 860 Medicare patients.
Identifying cognitive impairment within electronic health records (EHRs) is crucial not only for timely diagnoses but also for facilitating research. Information about cognitive impairment often exists within unstructured clinician notes in EHRs, but manual chart reviews are both time-consuming and error-prone. To address this issue, our study evaluates an automated approach using zero-shot GPT-4o to determine stage of cognitive impairment in two different tasks. First, we evaluated the ability of GPT-4o to determine the global Clinical Dementia Rating (CDR) on specialist notes from 769 patients who visited the memory clinic at Massachusetts General Hospital (MGH), and achieved a weighted kappa score of 0.83. Second, we assessed GPT-4o's ability to differentiate between normal cognition, mild cognitive impairment (MCI), and dementia on all notes in a 3-year window from 860 Medicare patients. GPT-4o attained a weighted kappa score of 0.91 in comparison to specialist chart reviews and 0.96 on cases that the clinical adjudicators rated with high confidence. Our findings demonstrate GPT-4o's potential as a scalable chart review tool for creating research datasets and assisting diagnosis in clinical settings in the future.