首个针对电子病历基础模型的临床意义评估基准，验证其在真实医疗场景中的表现。

Foundation models (FMs) promise to address core limitations of traditional supervised machine learning: (i) reliance on large amounts of labeled data, (ii) task specificity, and (iii) poor transportability. Despite methodological advances in structured electronic health record (EHR) foundation models, no systematic benchmark has validated whether these models meaningfully deliver on these promises. We introduce a benchmark of 14 clinically meaningful prediction tasks spanning patient prognosis and early diagnosis of acute and chronic conditions. We benchmark 6 state-of-the-art EHR FMs beyond population-level discrimination, emphasizing the need for evaluating their calibration and fairness, with rigorous controls for data contamination and reproducibility across more than 6 million patients from Columbia University Irving Medical Center and MIMIC-IV. Our benchmark identifies that FMs deliver on some of their promises. In particular, top-performing FMs outperform traditional baselines on discriminative performance, especially under limited labeled data, and exhibit more equitable performance across socio-medical groups. However, these models may underperform in low-prevalence settings, as pretraining losses may discard discriminative information about such conditions, and present lower calibration under limited labeled data. Further, cross-institutional transportability remains a challenge for structured EHR FMs. Together, these findings advance our understanding of EHR FMs' potential for clinical utility, highlight critical gaps that remain to be addressed, and provide a reproducible framework to track progress.