Mohsen Bayati

About

Mohsen Bayati is the Carl and Marilynn Thoma Professor of Operations, Information & Technology at Stanford University. He also holds courtesy appointments as a Professor of Electrical Engineering in the School of Engineering and as a Professor of Radiation Oncology in the School of Medicine. His academic and research interests encompass applied machine learning in healthcare, graphical models and message-passing algorithms, and the mathematics of learning and decision-making. His work aims to improve healthcare through data-driven learning and decision models, develop mathematical models and algorithms for experimentation, learning, and personalized decision-making, and advance statistical inference methods. Professor Bayati has been recognized with several awards, including the PhD Faculty Distinguished Service Award and multiple Stanford GSB Faculty Scholar titles. He is actively involved in research that spans various domains, including healthcare analytics, network models, and algorithmic decision-making, contributing to the development of innovative solutions in these fields.

Research topics

• Computer science
• Mathematics
• Mathematical optimization
• Algorithm
• Combinatorics

Selected publications

• Large language models for preventing medication direction errors in online pharmacies

  Nature Medicine · 2024-04-25 · 71 citations

  articleOpen accessSenior author

  Errors in pharmacy medication directions, such as incorrect instructions for dosage or frequency, can increase patient safety risk substantially by raising the chances of adverse drug events. This study explores how integrating domain knowledge with large language models (LLMs)-capable of sophisticated text interpretation and generation-can reduce these errors. We introduce MEDIC (medication direction copilot), a system that emulates the reasoning of pharmacists by prioritizing precise communica…

  PublisherOA PDFDOI

• Frustration With Technology and its Relation to Emotional Exhaustion Among Health Care Workers: Cross-sectional Observational Study

  Journal of Medical Internet Research · 2021-07-06 · 53 citations

  articleOpen access

  Background New technology adoption is common in health care, but it may elicit frustration if end users are not sufficiently considered in their design or trained in their use. These frustrations may contribute to burnout. Objective This study aimed to evaluate and quantify health care workers’ frustration with technology and its relationship with emotional exhaustion, after controlling for measures of work-life integration that may indicate excessive job demands. Methods This was a cross-sectio…

  PublisherOA PDFDOI

• Predicting Primary Care Physician Burnout From Electronic Health Record Use Measures

  Mayo Clinic Proceedings · 2024-04-04 · 11 citations

  articleOpen access

  OBJECTIVE: To evaluate the ability of routinely collected electronic health record (EHR) use measures to predict clinical work units at increased risk of burnout and potentially most in need of targeted interventions. METHODS: In this observational study of primary care physicians, we compiled clinical workload and EHR efficiency measures, then linked these measures to 2 years of well-being surveys (using the Stanford Professional Fulfillment Index) conducted from April 1, 2019, through October…

  PublisherOA PDFDOI

• Optimal Experimental Design for Staggered Rollouts

  Management Science · 2023-12-14 · 7 citations

  article

  In this paper, we study the design and analysis of experiments conducted on a set of units over multiple time periods in which the starting time of the treatment may vary by unit. The design problem involves selecting an initial treatment time for each unit in order to most precisely estimate both the instantaneous and cumulative effects of the treatment. We first consider nonadaptive experiments, in which all treatment assignment decisions are made prior to the start of the experiment. For this…

  PublisherDOI

• Qualitative Verification of Machine Learning-Based Burnout Predictors in Primary Care Physicians: An Exploratory Study

  Applied Clinical Informatics · 2025-04-28 · 3 citations

  articleOpen access

  Electronic health record (EHR) usage measures may quantify physician activity at scale and predict practice settings with a high risk for physician burnout, but their relation to experiences is poorly understood.This study aimed to explore the EHR-related experiences and well-being of primary care physicians in comparison to EHR usage measures identified as important for predicting burnout from a machine learning model.Exploratory qualitative study with semi-structured interviews of primary care…

  PublisherOA PDFDOI

Recent grants

• CAREER: Algorithms and Decision Models for Learning in Health Care Systems

  NSF · $500k · 2016–2022

• EAGER: Data-Driven Learning and Decision Making in Healthcare

  NSF · $300k · 2014–2017

• ICES: Small: Collaborative Research: Data-driven mechanisms in healthcare

  NSF · $200k · 2012–2015

Frequent coauthors

• Andrea Montanari

  27 shared

• Amin Saberi

  16 shared

• Guido W. Imbens

  Stanford University

  14 shared

• Susan Athey

  14 shared

• Jennifer Chayes

  14 shared

• Hamsa Bastani

  University of Pennsylvania

  14 shared

• Christian Borgs

  14 shared

• Khashayar Khosravi

  13 shared

Awards & honors

• PhD Faculty Distinguished Service Award, Stanford GSB, 2024
• Younger Family Faculty Scholar, Stanford GSB, 2020–21
• Younger Family Faculty Scholar, Stanford GSB, 2019–20
• Spence Faculty Scholar, Stanford GSB, 2015–16