# Scaling Electronic Health Record Foundation Models for Population Health Management

arXiv: https://arxiv.org/abs/2506.00209 · HTML: https://mlsanity.com/paper/2506.00209/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 74 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 24 of 24)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
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- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks](https://mlsanity.com/paper/2010.08240/) (2020)
- [EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models](https://mlsanity.com/paper/2307.02028/) (2023)
- [MOTOR: A Time-To-Event Foundation Model For Structured Medical Records](https://mlsanity.com/paper/2301.03150/) (2023)
- [Zero Shot Health Trajectory Prediction Using Transformer](https://mlsanity.com/paper/2407.21124/) (2024)
- [Biomedical Entity Representations with Synonym Marginalization](https://mlsanity.com/paper/2005.00239/) (2020)
- [CODER: Knowledge infused cross-lingual medical term embedding for term normalization](https://mlsanity.com/paper/2011.02947/) (2020)
- [Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events](https://mlsanity.com/paper/2306.11547/) (2023)
- [Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale](https://mlsanity.com/paper/2003.06516/) (2020)
- [Using Sequences of Life-events to Predict Human Lives](https://mlsanity.com/paper/2306.03009/) (2023)
- [A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?](https://mlsanity.com/paper/2304.04780/) (2023)
- [Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generation](https://mlsanity.com/paper/2407.15268/) (2024)
- [Linq-Embed-Mistral Technical Report](https://mlsanity.com/paper/2412.03223/) (2024)
- [Exploring Scaling Laws for EHR Foundation Models](https://mlsanity.com/paper/2505.22964/) (2025)
- [Foresight -- Generative Pretrained Transformer (GPT) for Modelling of Patient Timelines using EHRs](https://mlsanity.com/paper/2212.08072/) (2022)
- [ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance](https://mlsanity.com/paper/2402.13448/) (2024)

## Abstract (arXiv)

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.