Title: Beyond the Clinic: A Large-Scale Evaluation of Augmenting EHR with Wearable Data for Diverse Health Prediction Show Chinese title

Title: 超越临床：利用可穿戴数据增强电子健康记录在多种健康预测中的大规模评估

Abstract: Electronic health records (EHRs) provide a powerful basis for predicting the onset of health outcomes. Yet EHRs primarily capture in-clinic events and miss aspects of daily behavior and lifestyle containing rich health information. Consumer wearables, by contrast, continuously measure activity, heart rate, and sleep, and more, offering complementary signals that can fill this gap. Despite this potential, there has been little systematic evaluation of the benefit that wearable data can bring to health outcome prediction on top of EHRs. In this study, we present an extensible framework for multimodal health outcome prediction that integrates EHR and wearable data streams. Using data from the All of Us Program, we systematically compared the combination of different encoding methods on EHR and wearable data, including the traditional feature engineering approach, as well as foundation model embeddings. Across ten clinical outcomes, wearable integration consistently improved model performance relative to EHR-only baselines, e.g., average delta AUROC +5.8% for major depressive disorder, +10.7% for hypertension, and +12.2% for diabetes. On average across all ten outcomes, fusing EHRs with wearable features shows 8.9% improvement in AUROC. To our knowledge, this is the first large-scale evaluation of wearable-EHR fusion, underscoring the utility of wearable-derived signals in complementing EHRs and enabling more holistic, personalized health outcome predictions. Meanwhile, our analysis elucidates future directions for optimizing foundation models for wearable data and its integration with EHR data. Show Chinese abstract

Abstract: 电子健康记录（EHRs）为预测健康结果的发生提供了强大的基础。 然而，EHRs主要记录诊所内的事件，而忽略了日常行为和生活方式中的丰富健康信息。 相比之下，消费类可穿戴设备可以持续测量活动、心率和睡眠等，提供补充信号，可以填补这一空白。 尽管有这种潜力，但目前对可穿戴数据在EHR基础上对健康结果预测带来的好处进行系统评估的研究还很少。 在本研究中，我们提出了一种可扩展的多模态健康结果预测框架，该框架整合了EHR和可穿戴数据流。 使用All of Us项目的数据，我们系统地比较了不同编码方法在EHR和可穿戴数据上的组合，包括传统的特征工程方法以及基础模型嵌入。 在十个临床结果中，与仅使用EHR的基线相比，整合可穿戴数据始终提高了模型性能，例如，对于重度抑郁障碍平均AUROC提升+5.8%，高血压提升+10.7%，糖尿病提升+12.2%。 在所有十个结果的平均值上，将EHR与可穿戴特征融合使AUROC提升了8.9%。 据我们所知，这是首次对可穿戴设备-EHR融合的大规模评估，强调了可穿戴设备衍生信号在补充EHR和实现更全面、个性化的健康结果预测方面的价值。 同时，我们的分析明确了优化基础模型以处理可穿戴数据及其与EHR数据集成的未来方向。