X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models

• Foundation Models for Electronic Health Records (FEMRs) are pretrained on large-scale structured patient data, enabling them to convert longitudinal patient trajectories into generalizable representations for diverse clinical prediction tasks. Despite their effectiveness, FEMRs remain black-box models, raising concerns about bias, interpretability, and clinical trust. To address this, we propose the first token-level explainability approach for FEMRs. We train a Transformer-based surrogate model on input-output pairs from the FEMR across two prediction tasks, approximating its behavior while preserving temporal dynamics. We identify the most influential tokens, providing insights into how FEMRs leverage different aspects of patient history for predictions. To evaluate clinical relevance, we introduce a novel clinical alignment metric that quantifies the correspondence between the surrogate model’s key tokens and clinically validated features. Our results demonstrate that the surrogate closely approximates FEMR predictions and that token-level explanations align well with clinical knowledge, offering a practical framework for interpretable and trustworthy clinical AI.

Reference: Huang, J.^†, Yin, P.^†, Xu, Z., Capurro, D., Conway, M., & Dang, T. (2026). X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models. arXiv preprint arXiv:2607.06163.
^† These authors contributed equally. Accepted by In Proceedings of International Joint Conferences on Artificial Intelligence(IJCAI 2026)(AI and Health Special Track, Accept Rate:18%)

Measuring and Visualizing Healthcare Process Variability

• Understanding factors that contribute to clinical variability in patient care is critical, as unwarranted variability can lead to increased adverse events and prolonged hospital stays. Determining when this variability becomes excessive can be a step in optimizing patient outcomes and healthcare efficiency. In this study, we presents a standardized way to measure and visualize variability in clinical processes and measure its impact on patient-relevant outcomes.

Reference: Yin, P., Cervantes, A. A., & Capurro, D. (2025). Measuring and visualizing healthcare process variability. Journal of biomedical informatics, 104918. Advance online publication. https://doi.org/10.1016/j.jbi.2025.104918