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

Will Ke Wang, Rui Yang, Chao Pang, Karthik Natarajan, Nan Liu, Daniel McDuff, David J. Slotwiner, Fei Wang, Matthew B.A. McDermott, Xuhai Xu
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:295-309, 2026.

Abstract

Electronic health records ({EHR}s) provide a powerful basis for predicting the onset of health outcomes. Yet {EHR}s 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, 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 {EHR}s. 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} +6.8% for major depressive disorder, +9.7% for hypertension, and +12.6% for diabetes. On average across all ten outcomes, fusing {EHR}s with wearable features shows 8.5% 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 {EHR}s 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-wang26a, title = {Beyond the Clinic: A Large-Scale Evaluation of Augmenting {EHR} with Wearable Data for Diverse Health Prediction}, author = {Wang, Will Ke and Yang, Rui and Pang, Chao and Natarajan, Karthik and Liu, Nan and McDuff, Daniel and Slotwiner, David J. and Wang, Fei and McDermott, Matthew B.A. and Xu, Xuhai}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {295--309}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/wang26a/wang26a.pdf}, url = {https://proceedings.mlr.press/v297/wang26a.html}, abstract = {Electronic health records ({EHR}s) provide a powerful basis for predicting the onset of health outcomes. Yet {EHR}s 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, 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 {EHR}s. 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} +6.8% for major depressive disorder, +9.7% for hypertension, and +12.6% for diabetes. On average across all ten outcomes, fusing {EHR}s with wearable features shows 8.5% 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 {EHR}s 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.} }
Endnote
%0 Conference Paper %T Beyond the Clinic: A Large-Scale Evaluation of Augmenting EHR with Wearable Data for Diverse Health Prediction %A Will Ke Wang %A Rui Yang %A Chao Pang %A Karthik Natarajan %A Nan Liu %A Daniel McDuff %A David J. Slotwiner %A Fei Wang %A Matthew B.A. McDermott %A Xuhai Xu %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-wang26a %I PMLR %P 295--309 %U https://proceedings.mlr.press/v297/wang26a.html %V 297 %X Electronic health records ({EHR}s) provide a powerful basis for predicting the onset of health outcomes. Yet {EHR}s 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, 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 {EHR}s. 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} +6.8% for major depressive disorder, +9.7% for hypertension, and +12.6% for diabetes. On average across all ten outcomes, fusing {EHR}s with wearable features shows 8.5% 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 {EHR}s 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.
APA
Wang, W.K., Yang, R., Pang, C., Natarajan, K., Liu, N., McDuff, D., Slotwiner, D.J., Wang, F., McDermott, M.B. & Xu, X.. (2026). Beyond the Clinic: A Large-Scale Evaluation of Augmenting EHR with Wearable Data for Diverse Health Prediction. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:295-309 Available from https://proceedings.mlr.press/v297/wang26a.html.

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