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

arXiv: https://arxiv.org/abs/2311.11483 · HTML: https://mlsanity.com/paper/2311.11483/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022)
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1906.12340/) (2019)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [BEHRT: Transformer for Electronic Health Records](https://mlsanity.com/paper/1907.09538/) (2019)
- [HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution](https://mlsanity.com/paper/2306.15794/) (2023)
- [Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models](https://mlsanity.com/paper/2307.14430/) (2023)
- [EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models](https://mlsanity.com/paper/2307.02028/) (2023)
- [Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data](https://mlsanity.com/paper/2001.05295/) (2020)
- [MOTOR: A Time-To-Event Foundation Model For Structured Medical Records](https://mlsanity.com/paper/2301.03150/) (2023)
- [GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning](https://mlsanity.com/paper/2207.09858/) (2022)
- [Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding](https://mlsanity.com/paper/2108.03625/) (2021)
- [No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging](https://mlsanity.com/paper/2307.16526/) (2023)

## Built on by (most cited first, 30 of 31)

- [MIRA: Medical Time Series Foundation Model for Real-World Health Data](https://mlsanity.com/paper/2506.07584/) (2025)
- [Foundation models for electronic health records: representation dynamics and transferability](https://mlsanity.com/paper/2504.10422/) (2025)
- [Tokenization Tradeoffs in Structured EHR Foundation Models](https://mlsanity.com/paper/2603.15644/) (2026)
- [Toward Clinically Explainable AI for Medical Diagnosis: A Foundation Model with Human-Compatible Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2509.03906/) (2025)
- [An Investigation of Memorization Risk in Healthcare Foundation Models](https://mlsanity.com/paper/2510.12950/) (2025)
- [One Loss to Rule Them All: Marked Time-to-Event for Structured EHR Foundation Models](https://mlsanity.com/paper/2602.00541/) (2026)
- [Towards Foundation Models for Critical Care Time Series](https://mlsanity.com/paper/2411.16346/) (2024)
- [MedRep: Medical Concept Representation for General Electronic Health Record Foundation Models](https://mlsanity.com/paper/2504.08329/) (2025)
- [Medical world models in healthcare: foundations, applications, and challenges for trustworthy clinical translation](https://mlsanity.com/paper/2607.25242/) (2026)
- [Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium](https://mlsanity.com/paper/2403.01628/) (2024)
- [Foundation Models for Clinical Records at Health System Scale](https://mlsanity.com/paper/2507.00574/) (2025)
- [Count-Based Approaches Remain Strong: A Benchmark Against Transformer and LLM Pipelines on Structured EHR](https://mlsanity.com/paper/2511.00782/) (2025)
- [The Taxonomies, Training, and Applications of Event Stream Modelling for Electronic Health Records](https://mlsanity.com/paper/2603.14003/) (2026)
- [One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligence](https://mlsanity.com/paper/2506.10157/) (2025)
- [PORTER: Language-Grounded Event Representations for Portable Structured EHR Foundation Models](https://mlsanity.com/paper/2606.24102/) (2026)
- [Prompting Large Language Models for Supporting the Differential Diagnosis of Anemia](https://mlsanity.com/paper/2409.15377/) (2024)
- [Learning Longitudinal Health Representations from EHR and Wearable Data](https://mlsanity.com/paper/2601.12227/) (2026)
- [Multi-lingual Multi-institutional Electronic Health Record based Predictive Model](https://mlsanity.com/paper/2604.00027/) (2026)
- [Protecting patient privacy in clinical foundation models: Technical and legal perspectives](https://mlsanity.com/paper/2608.07705/) (2026)
- [Beyond One-Time Validation: A Framework for Adaptive Validation of Prognostic and Diagnostic AI-based Medical Devices](https://mlsanity.com/paper/2409.04794/) (2024)
- [General Demographic Pre-trained Models for Enhancing Predictive Performance Across Diseases and Population](https://mlsanity.com/paper/2509.07330/) (2025)
- [A Common Pipeline for Harmonizing Electronic Health Record Data for Translational Research](https://mlsanity.com/paper/2509.08553/) (2025)
- [EpiScreen: Early Epilepsy Detection from Electronic Health Records with Large Language Models](https://mlsanity.com/paper/2603.28698/) (2026)
- [A Nationwide Japanese Medical Claims Foundation Model: Balancing Model Scaling and Task-Specific Computational Efficiency](https://mlsanity.com/paper/2604.22348/) (2026)
- [Beyond Forgetting in Continual Medical Image Segmentation: A Comprehensive Benchmark Study](https://mlsanity.com/paper/2605.06160/) (2026)
- [From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction](https://mlsanity.com/paper/2605.16927/) (2026)
- [X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models](https://mlsanity.com/paper/2607.06163/) (2026)
- [Federated generative event models for tokenized electronic health records](https://mlsanity.com/paper/2608.02939/) (2026)
- [A Multimodal Foundation Model for Longitudinal Patient Representation and Scalable Insight Generation in Oncology](https://mlsanity.com/paper/2608.24688/) (2026)
- [Learning Continuous Patient Trajectories from Electronic Health Records](https://mlsanity.com/paper/2609.36144/) (2026)

## Abstract (arXiv)

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 (FMSM), 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 FMSM 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 FMSM 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.