Multi-Hospital Electronic Health Record Foundation Models Without Data Sharing: A Comparison of Federated Learning and Inference-Time Ensembling
Abstract
BACKGROUND: Foundation models for electronic health records (EHRs) perform strongly on clinical prediction, but every published model has been trained within a single health system. No multi-institutional EHR foundation model currently exists, largely because privacy regulations and governance barriers block data pooling across hospitals. Two strategies could build such models without pooling: federated learning (exchanges model weights) and inference-time ensembling (exchanges only predictions at query time). Whether either is viable for autoregressive EHR foundation models, and whether individual hospitals benefit from participating, is not established.
METHODS: We trained a generative pretrained transformer (GPT) style EHR foundation model on 100,163 Medical Information Mart for Intensive Care (MIMIC-IV) patients, partitioned into five heterogeneously distributed (non-IID) sites by Dirichlet allocation over International Classification of Diseases (ICD) chapters. We compared centralized training, federated averaging, and inference-time ensembling, and each hospital's solo model against the ensemble including it. Models were evaluated on 15,012 held-out patients using per-condition area under the receiver operating characteristic curve (AUROC) for five acute conditions and micro-averaged area under the precision-recall curve (AUPRC) across 2,590 diagnoses.
RESULTS: Centralized training achieved per-condition AUROC 0.75-0.85 and overall AUPRC 0.376. Federated averaging…
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