Faculty and ResearchExpert Guide

Min Chen

Min Chen

SunTrust Bank Professorship

Associate Professor
Department of Information Systems and Business Analytics

College of Business
Florida International University

Modesto A. Maidique Campus
11200 S.W. 8th St, RB 204A
Miami, FL 33199

(305) 348-4201


  • Ph.D. in Managerial Economics and Strategy
    Northwestern University, Evanston, Illinois
  • Master of Public Policy
    University of Chicago, Chicago, Illinois
  • Bachelor in Economics
    Renmin University of China, Beijing, China

Areas of Expertise

  • Artificial Intelligence
  • Big Data Analytics
  • Health IT and Policy

Professional Activities

Dr. Min Chen’s research examines information technology innovations, healthcare analytics, and issues relevant to the economics, organization, and regulation of the U.S. health care system. She uses large-scale datasets and state-of-the-art techniques to predict disease incidence, measure policy impact and examine innovations aiming to improve healthcare quality and reduce costs. Dr. Chen’s research has been published widely in high impact peer-reviewed journals across disciplines, recognized with competitive awards, and featured in media outlets.

Prior to joining FIU, Dr. Chen worked as an economic consultant at Charles River Associates and advised on antitrust issues in a range of industries (e.g., telecommunications, tobacco, hospitals, and pharmaceuticals). She combines her academic knowledge and practical experiences to develop both face-to-face and online MBA courses and all her online courses successfully achieved Quality Matters credentials. She received the best professor and best course awards from FIU's Healthcare MBA program and was invited to address various stakeholders about the challenges and opportunities facing the U.S. health care systems.

Selected Recent Media Coverage:

  • Palm Beach Post – May 8, 2023
    Researchers from Florida International University’s College of Business have been instrumental in developing a machine-learning (ML) algorithm that uses hospital data and social determinants of health data that can help diagnose a stroke quickly — before laboratory test results or diagnostic images were available. “Our algorithm can incorporate a lot of variables to analyze and interpret complex patterns,” said Min Chen, professor of information systems and business analytics.
  • Fox News – April 21, 2023
    A story described research from Florida International University, Carnegie Mellon University and Santa Clara University which showed that using machine learning methods and available data when patients enter the hospital, researchers have developed a model that predicts strokes with more accuracy than current models. The study's authors sought to develop a stroke-prediction algorithm and their model incorporated variables routinely collected by health care providers and payers.
  • The Floridian – April 21, 2023
    A story examined an FIU Business professor’s new algorithmic technology that diagnoses strokes with an 84% success rate. The research project that has taken four years to develop seems to be making finalizing headway. As of right now, the new technology is being implemented experimentally across emergency rooms. Min Chen, associate professor of information systems and business analytics, who led the research, described the project as, "more like human and machine collaboration," in the article.
  • NBC 6 Miami - April 20, 2023
    A story highlighted research from Min Chen, associate professor of information systems and business analytics, who has been working on an algorithm that could transform the medical field — one that could quickly diagnose strokes. The algorithm uses hospital data and social determinants of health data to diagnose a stroke before lab results or diagnostic images are available. “It’s more like human and machine collaboration,” Chen said.
    Watch It
  • Sun Sentinel – April 19, 2023
    A story noted an FIU Business-created algorithm uses hospital data and social determinants of health data to predict strokes with greater accuracy. “Our algorithm can incorporate a lot of variables to analyze and interpret complex patterns,” said Min Chen, associate professor of information systems and business analytics, in the article.
  •  Health IT Analytics - Apr 12, 2023
    Published in the Journal of Medical Internet Research, findings from Florida International University’s College of Business (FIU Business) indicate that a new machine-learning (ML) algorithm could leverage hospital and social determinants of health (SDoH) data to improve the speed and accuracy of stroke diagnoses.
  • HospiMedica – April 11, 2023
  • Health Tech Hot Spot – April 4, 2023
  • eeNews Europe – April 4, 2023 Article
  • Clinical Lab Products – April 3, 2023 Article
  • Healthcare Information and Management Systems Society (HIMSS22) Women Leaders in Health IT Roundtable:
    Segment 1 – Career Paths Watch It
    Segment 2 – How to Become a Thought Leader Watch It
    Segment 3 – Tech Trends Watch It

Courses Taught

  • Business Statistics and Analysis I
  • Clinical Information Systems
  • Competitive Strategy
  • Economic and Decision Analysis in Health Services
  • Health Policy and Economics
  • Managerial Decision-Making in Health Economics

Refereed Journal Articles

Cousins, K. C., Hertelendy, A. J., Chen, M., Durneva, P., & Wang, S.


Building Resilient Hospital Information Technology Services through Organizational Learning: Lessons in CIO Leadership During an International Systemic Crisis in the United States and Abu Dhabi, United Arab Emirates.

International Journal of Medical Informatics



Chen, M., Tan, X., & Padman, R.


A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study.

Journal of Medical Internet Research


View Article

Chen, M., Tan, X., & Padman, R. (2020). Social Determinants of Health in Electronic Health Records and Their Impact on Analysis and Risk Prediction: A Systematic Review. Journal of the American Medical Informatics Association, 27(11). View Article

Durneva, P., Karlene, C., & Chen, M. (2020). Blockchain Technology in Patient Care: Current State of Research, Challenges and Future Research Directions. Journal of the Medical Internet Research, 22(7).

Chen, M., & Grabowski, D. C. (2019). Hospital Readmissions Reduction Program: Intended and Unintended Effects. Medical Care Research and Review, 76(5). View Article

Chen, M., Guo, S., & Tan, X. (2019). Does Health Information Exchange Improve Patient Outcomes? Empirical Evidence from Florida Hospitals. Health Affairs, 38(2). View Article

Chen, M. (2018). Reducing Excess Hospital Readmissions: Does Destination Matter? International Journal of Health Economics and Management, 18(1). View Article

Chen, M., Tremblay, M., & Lukyanenko, R. (2017). Information Quality Challenges in Shared Decision Making. ACM Journal of Data and Information Quality, 9(5). View Article

Chen, M. (2015). The Affordable Care Act and the Future of Physicians: The Opportunities and Challenges. Journal of Craniofacial Surgery, 26(8). View Article

Chen, M., & Grabowski, D. C. (2015). Intended and Unintended Consequences of Minimum Staffing Standards for Nursing Homes. Health Economics, 24(7). View Article

Sojourner, A., Frandsen, B., Town, R., Grabowski, D., & Chen, M. (2015). Impacts of Unionization on Quality and Productivity: Regression Discontinuity Evidence from Nursing Homes. Industrial and Labor Relations Review, 68(4). View Article

Chen, M., & Serfes, K. (2012). Minimum Quality Standard Regulation under Imperfect Quality Observability. Journal of Regulatory Economics, 41(2). View Article

Sojourner, A. J., Grabowski, D. C., Chen, M., & Town, R. J. (2011). Trends in unionization of nursing homes. Inquiry: The Journal of Health Care Organization, Provision, and Financing, 47(4). View Article

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