A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records

Understand

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.