Foundation models hold promise for transforming AI in healthcare by providing
modular components that are easily adaptable to downstream healthcare tasks,
making AI development more scalable and cost-effective. Structured EHR
foundation models, trained on coded medical records from millions of patients,
demonstrated benefits including increased performance with fewer training
labels, and improved robustness to distribution shifts. However, questions
remain on the feasibility of sharing these models across different hospitals
and their performance for local task adaptation. This multi-center study
examined the adaptability of a recently released structured EHR foundation
model ($FM_{SM}$), trained on longitudinal medical record data from 2.57M
Stanford Medicine patients. Experiments were conducted using EHR data at The
Hospital for Sick Children and MIMIC-IV. We assessed both adaptability via
continued pretraining on local data, and task adaptability compared to
baselines of training models from scratch at each site, including a local
foundation model. We evaluated the performance of these models on 8 clinical
prediction tasks. In both datasets, adapting the off-the-shelf $FM_{SM}$
matched the performance of GBM models locally trained on all data while
providing a 13% improvement in settings with few task-specific training labels.
With continued pretraining on local data, label efficiency substantially
improved, such that $FM_{SM}$ required fewer than 1% of training examples to
match the fully trained GBM's performance. Continued pretraining was also 60 to
90% more sample-efficient than training local foundation models from scratch.
Our findings show that adapting shared EHR foundation models across hospitals
provides improved prediction performance at less cost, underscoring the utility
of base foundation models as modular components to streamline the development
of healthcare AI.

通过将基础模型应用于医疗保健领域，可使人工智能的开发更具规模性和经济效益。医疗记录的结构化基础模型在数百万患者的记录上训练，表现出了较少训练标签的性能提升以及对分布变化的改进鲁棒性。本研究基于多中心数据研究探讨了这种基础模型的适应性，结果显示在医院间分享模型可提高预测性能并降低成本，进而强调基础模型作为模块化组件在健康保健人工智能开发中的实用性。