Large language models to identify social determinants of health in electronic health records

Guevara-Vega, Marco; Chen, Shan; Thomas, Spencer A.; Chaunzwa, Tafadzwa L.; Franco, Idalid; Kann, Benjamin H.; Moningi, Shalini; Qian, Jack M.; Goldstein, Madeleine; Harper, Susan; Aerts, Hugo J.W.L.; Catalano, Paul J.; Savova, Guergana; Mak, Raymond H.; Bitterman, Danielle S.

doi:10.1038/s41746-023-00970-0

articlenpj Digital MedicineJan 11, 2024GOLD OA

Large language models to identify social determinants of health in electronic health records

MGMarco Guevara-Vega SCShan Chen SASpencer A. Thomas TLTafadzwa L. Chaunzwa IFIdalid Franco

Brigham and Women's Hospital · Harvard University · +6 more institutions

PubMed

Indexed incrossrefdoajpubmed

Abstract

Social determinants of health (SDoH) play a critical role in patient outcomes, yet their documentation is often missing or incomplete in the structured data of electronic health records (EHRs). Large language models (LLMs) could enable high-throughput extraction of SDoH from the EHR to support research and clinical care. However, class imbalance and data limitations present challenges for this sparsely documented yet critical information. Here, we investigated the optimal methods for using LLMs to extract six SDoH categories from narrative text in the EHR: employment, housing, transportation, parental status, relationship, and social support. The best-performing models were fine-tuned Flan-T5 XL for any SDoH…

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245

total citations

FWCI: 85.39
Percentile: 100%
References: 58

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Authors

15

Topics & keywords

Topics

Keywords

Social determinants of health
Health care
Medicine
Political science
Law

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