Bates, David W.

Zero-shot interpretable phenotyping of postpartum hemorrhage using large language models

Alsentzer, E., Rasmussen, M. J., Fontoura, R., Cull, A. L., Beaulieu-Jones, B., Gray, K. J., Bates, D. W., & Kovacheva, V. P. (2023) · npj Digital Medicine

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 69

Enhancing early detection of cognitive decline in the elderly: a comparative study utilizing large language models in clinical notes

Du, X., Novoa-Laurentiev, J., Plasek, J. M., Chuang, Y.-W., Wang, L., Marshall, G. A., Mueller, S. K., Chang, F., Datta, S., Paek, H., et al. (2024) · eBioMedicine

Machine Learning in HealthcareDementia and Cognitive Impairment Research

Cited by 69

Sleep and Alertness in a Duty-Hour Flexibility Trial in Internal Medicine

Basner, M., Asch, D. A., Shea, J. A., Bellini, L. M., Carlin, M., Ecker, A. J., Malone, S. K., Desai, S. V., Sternberg, A. L., Tonascia, J., et al. (2019) · New England Journal of Medicine

Sleep and Work-Related FatigueHospital Admissions and Outcomes

Cited by 69

E-Prescribing Collaboration in Massachusetts: Early Experiences from Regional Prescribing Projects

Halamka, J., Aranow, M., Ascenzo, C., Bates, D. W., Berry, K., Debor, G., Fefferman, J., Glaser, J., Heinold, J., Stanley, J., et al. (2006) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsTechnology and Human Factors in Education and Health

Cited by 68

Predicting pressure injury using nursing assessment phenotypes and machine learning methods

Song, W., Kang, M.-J., Zhang, L., Jung, W., Song, J., Bates, D. W., & Dykes, P. C. (2021) · Journal of the American Medical Informatics Association

Pressure Ulcer Prevention and ManagementMachine Learning in Healthcare

Cited by 67

Organizational strategies for promoting patient and provider uptake of personal health records

Wells, S., Rozenblum, R., Park, A., Dunn, M., & Bates, D. W. (2015) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsMobile Health and mHealth Applications

Cited by 67

Digital health and patient safety

Agboola, S. O., Bates, D. W., & Kvedar, J. C. (2016) · JAMA

Artificial Intelligence in Healthcare and EducationPatient Safety and Medication Errors

Cited by 64

Association between workarounds and medication administration errors in bar-code-assisted medication administration in hospitals

van der Veen, W., van den Bemt, P. M., Wouters, H., Bates, D. W., Twisk, J. W., de Gier, J. J., Taxis, K., for the BCMA Study Group, Duyvendak, M., Luttikhuis, K. O., et al. (2018) · Journal of the American Medical Informatics Association

Patient Safety and Medication ErrorsElectronic Health Records Systems

Cited by 64

High-priority drug-drug interaction clinical decision support overrides in a newly implemented commercial computerized provider order-entry system: Override appropriateness and adverse drug events

Edrees, H., Amato, M. G., Wong, A., Seger, D. L., & Bates, D. W. (2020) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsPharmaceutical Practices and Patient Outcomes

Cited by 64

A scoping review on generative AI and large language models in mitigating medication related harm

Ong, J. C. L., Chen, M. H., Ng, N., Elangovan, K., Tan, N. Y. T., Jin, L., Xie, Q., Ting, D. S. W., Rodriguez-Monguio, R., Bates, D. W., et al. (2025) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationPharmacovigilance and Adverse Drug Reactions

Cited by 64

Patient safety and artificial intelligence in clinical care

Ratwani, R. M., Bates, D. W., & Classen, D. C. (2024) · JAMA Health Forum

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 63

Physicians' Use of Key Functions in Electronic Health Records from 2005 to 2007: A Statewide Survey

Simon, S. R., Soran, C. S., Kaushal, R., Jenter, C. A., Volk, L. A., Burdick, E., Cleary, P. D., Orav, E. J., Poon, E. G., & Bates, D. W. (2009) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsMedical Coding and Health Information

Cited by 61

Unexpected hypoglycemia in a critically ill patient.

Bates, D. W. (2002) · Annals of Internal Medicine

Patient Safety and Medication ErrorsClinical Reasoning and Diagnostic Skills

Cited by 58

Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial

Rossetti, S. C., Dykes, P. C., Knaplund, C., Cho, S., Withall, J., Lowenthal, G., Albers, D., Lee, R. Y., Jia, H., Bakken, S., et al. (2025) · Nature Medicine

Healthcare Technology and Patient MonitoringMachine Learning in Healthcare

Cited by 54

Using artificial intelligence to improve primary care for patients and clinicians.

Sarkar, U., & Bates, D. W. (2024) · JAMA Internal Medicine

Artificial Intelligence in Healthcare and EducationElectronic Health Records Systems

Cited by 52

Medication safety messages for patients via the web portal: The MedCheck intervention

Weingart, S. N., Hamrick, H. E., Tutkus, S., Carbo, A., Sands, D. Z., Tess, A., Davis, R. B., Bates, D. W., & Phillips, R. S. (2008) · International Journal of Medical Informatics

Patient Safety and Medication ErrorsPharmaceutical Practices and Patient Outcomes

Cited by 51

The Value of Provider-to-Provider Telehealth

Pan, E., Cusack, C., Hook, J., Vincent, A., Kaelber, D. C., Bates, D. W., & Middleton, B. (2008) · Telemedicine and e-Health

Telemedicine and Telehealth ImplementationHealth Systems, Economic Evaluations, Quality of Life

Cited by 46

Renal medication-related clinical decision support (CDS) alerts and overrides in the inpatient setting following implementation of a commercial electronic health record: implications for designing more effective alerts

Shah, S. N., Amato, M. G., Garlo, K. G., Seger, D. L., & Bates, D. W. (2021) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsPharmaceutical Practices and Patient Outcomes

Cited by 43

Standard practices for computerized clinical decision support in community hospitals: a national survey

Ash, J. S., McCormack, J. L., Sittig, D. F., Wright, A., McMullen, C., & Bates, D. W. (2012) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsArtificial Intelligence in Healthcare and Education

Cited by 41

A Clinically Integrated mHealth App and Practice Model for Collecting Patient-Reported Outcomes between Visits for Asthma Patients: Implementation and Feasibility

Rudin, R. S., Fanta, C. H., Qureshi, N., Duffy, E., Edelen, M. O., Dalal, A. K., & Bates, D. W. (2019) · Applied Clinical Informatics

Mobile Health and mHealth ApplicationsAsthma and respiratory diseases

Cited by 37

Improving Electronic Health Record Usability and Safety Requires Transparency

Ratwani, R. M., Hodgkins, M., & Bates, D. W. (2018) · JAMA

Electronic Health Records SystemsMedical Coding and Health Information

Cited by 36

Leveraging large language models to foster equity in healthcare

Rodriguez, J. A., Alsentzer, E., & Bates, D. W. (2024) · Journal of the American Medical Informatics Association

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 32

Differences in clinician electronic health record use across adult and pediatric primary care specialties

Rotenstein, L. S., Holmgren, A. J., Downing, N. L., Longhurst, C. A., & Bates, D. W. (2021) · JAMA Network Open

Electronic Health Records SystemsMedical Coding and Health Information

Cited by 31

Standardizing and Scaffolding Health Care AI-Chatbot Evaluation: Systematic Review

Hua, Y., Xia, W., Bates, D., Hartstein, G. L., Kim, H. T., Li, M., Nelson, B. W., Stromeyer IV, C., King, D., Suh, J., et al. (2025) · JMIR AI

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 30