Scaling Electronic Health Record Foundation Models for Population Health Management

Abstract:Population health management requires scalable methods to identify individuals at risk of chronic diseases such as cardiovascular conditions and cancer, yet existing approaches rely on fragmented data and resource-intensive screening. We present Scaling Electronic Health Record Foundation Models for Population Health Management, an Electronic Health Record Foundation Model that performs large-scale chronic disease prediction using cross-site longitudinal medical records. We pretrain Scaling Electronic Health Record Foundation Models for Population Health Management on billions of medical events from over 5 million patients across Taiwan and the United States, leveraging a unified code alignment framework to address cross-system heterogeneity, and characterize its scaling behavior via IsoFLOP analysis, training compute-optimal models up to 2.4B parameters. Across 11 chronic disease prediction tasks, Scaling Electronic Health Record Foundation Models for Population Health Management demonstrates strong scaling and generalization, outperforming tree-based models and both general and biomedical language models, achieving over 40% and 70% sensitivity at 99% specificity in U.S. and Taiwan cohorts, respectively. On the EHRShot benchmark, Scaling Electronic Health Record Foundation Models for Population Health Management surpasses prior EHR foundation models trained on in-site data despite substantial distribution shifts, highlighting strong few-shot generalization. Finally, we show that aligned cross-system data provides more effective pretraining signal than duplicating single-site data under data-limited settings, underscoring the importance of alignment for scalable healthcare modeling. Our analysis demonstrates the robustness of EHR-FM in various patient distributions and the benefits of operating in the ICD code space. The code will be open-sourced.

Machine Learning (cs.LG); Computation and Language (cs.CL)

arXiv:2506.00209 [cs.LG]

(or arXiv:2506.00209v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2506.00209 Focus to learn more arXiv-issued DOI via DataCite

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