Sharp, Christopher

Ambient artificial intelligence scribes: physician burnout and perspectives on usability and documentation burden.

Shah, S. J., Devon-Sand, A., Ma, S. P., Jeong, Y., Crowell, T., Smith, M., Liang, A. S., Delahaie, C., Hsia, C., Shanafelt, T., et al. (2025) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsArtificial Intelligence in Healthcare and Education

Cited by 224

Metrics for assessing physician activity using electronic health record log data

Sinsky, C. A., Rule, A., Cohen, G., Arndt, B. G., Shanafelt, T. D., Sharp, C. D., Baxter, S. L., Tai-Seale, M., Yan, S., Chen, Y., et al. (2020) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsMedical Coding and Health Information

Cited by 162

AI-Generated Draft Replies Integrated Into Health Records and Physicians’ Electronic Communication

Tai-Seale, M., Baxter, S. L., Vaida, F., Walker, A., Sitapati, A. M., Osborne, C., Diaz, J., Desai, N., Webb, S., Polston, G., et al. (2024) · JAMA Network Open

Artificial Intelligence in Healthcare and EducationElectronic Health Records Systems

Cited by 158

Ambient artificial intelligence scribes: utilization and impact on documentation time

Ma, S. P., Liang, A. S., Shah, S. J., Smith, M., Jeong, Y., Devon-Sand, A., Crowell, T., Delahaie, C., Hsia, C., Lin, S., et al. (2025) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsArtificial Intelligence in Healthcare and Education

Cited by 155

MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

Fleming, S. L., Lozano, A., Haberkorn, W. J., Jindal, J. A., Reis, E., Thapa, R., Blankemeier, L., Genkins, J. Z., Steinberg, E., Nayak, A., et al. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 66

MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

Fleming, S. L., Lozano, A., Haberkorn, W. J., Jindal, J. A., Reis, E. P., Thapa, R., Blankemeier, L., Genkins, J. Z., Steinberg, E., Nayak, A., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 65

Standing on FURM Ground: A Framework for Evaluating Fair, Useful, and Reliable AI Models in Health Care Systems

Callahan, A., McElfresh, D., Banda, J. M., Bunney, G., Char, D., Chen, J., Corbin, C. K., Dash, D., Downing, N. L., Jain, S. S., et al. (2024) · NEJM Catalyst

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 61

Implementation and evaluation of Stanford Health Care store-and-forward teledermatology consultation workflow built within an existing electronic health record system

Kim, G. E., Afanasiev, O. K., O’Dell, C., Sharp, C., & Ko, J. M. (2020) · Journal of Telemedicine and Telecare

Telemedicine and Telehealth ImplementationHealthcare Systems and Technology

Cited by 23

Effect of an Electronic Health Record-Based Intervention on Documentation Practices

Shah, S., Bedgood, M., Devon-Sand, A., Dolphin-Dempsey, C., Cherukuri, V., Weng, K., Lin, S., & Sharp, C. (2024) · Applied Clinical Informatics

Electronic Health Records SystemsMedical Coding and Health Information

Cited by 4

SmartAlert - Implementing Machine Learning-Driven Clinical Decision Support for Inpatient Laboratory Utilization Reduction

Liang, A. S., Amrollahi, F., Jiang, Y., Corbin, C. K., Kim, G. Y. E., Mui, D., Crowell, T., Acharya, A., Mony, S., Punnathanam, S., et al. (2026) · NEJM AI

Electronic Health Records SystemsArtificial Intelligence in Healthcare and Education

Cited by 0