The shaky foundations of large language models and foundation models for electronic health records

Michael Wornow; Yizhe Xu; Rahul Thapa; Birju Patel; Ethan Steinberg; Scott Fleming; Michael A Pfeffer; Jason Fries; Nigam H Shah

doi:10.1038/s41746-023-00879-8

The shaky foundations of large language models and foundation models for electronic health records

NPJ Digit Med. 2023 Jul 29;6(1):135. doi: 10.1038/s41746-023-00879-8.

Authors

Michael Wornow¹, Yizhe Xu², Rahul Thapa², Birju Patel², Ethan Steinberg³, Scott Fleming², Michael A Pfeffer^{2

4}, Jason Fries², Nigam H Shah^{2

4

5

6}

Affiliations

¹ Department of Computer Science, Stanford University, Stanford, CA, USA. mwornow@stanford.edu.
² Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA.
³ Department of Computer Science, Stanford University, Stanford, CA, USA.
⁴ Technology and Digital Services, Stanford Health Care, Palo Alto, CA, USA.
⁵ Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
⁶ Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA.

Abstract

The success of foundation models such as ChatGPT and AlphaFold has spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models' capabilities. In this narrative review, we examine 84 foundation models trained on non-imaging EMR data (i.e., clinical text and/or structured data) and create a taxonomy delineating their architectures, training data, and potential use cases. We find that most models are trained on small, narrowly-scoped clinical datasets (e.g., MIMIC-III) or broad, public biomedical corpora (e.g., PubMed) and are evaluated on tasks that do not provide meaningful insights on their usefulness to health systems. Considering these findings, we propose an improved evaluation framework for measuring the benefits of clinical foundation models that is more closely grounded to metrics that matter in healthcare.

Publication types

Review