Considering the Secondary Use of Clinical and Educational Data to Facilitate the Development of Artificial Intelligence Models.

Journal: Academic medicine : journal of the Association of American Medical Colleges
PMID: 38109656

Abstract

Medical training programs and health care systems collect ever-increasing amounts of educational and clinical data. These data are collected with the primary purpose of supporting either trainee learning or patient care. Well-established principles guide the secondary use of these data for program evaluation and quality improvement initiatives. More recently, however, these clinical and educational data are also increasingly being used to train artificial intelligence (AI) models. The implications of this relatively unique secondary use of data have not been well explored. These models can support the development of sophisticated AI products that can be commercialized. While these products have the potential to support and improve the educational system, there are challenges related to validity, patient and learner consent, and biased or discriminatory outputs. The authors consider the implications of developing AI models and products using educational and clinical data from learners, discuss the uses of these products within medical education, and outline considerations that should guide the appropriate use of data for this purpose. These issues are further explored by examining how they have been navigated in an educational collaborative.

Authors

  • Brent Thoma
    B. Thoma is associate professor, Department of Emergency Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada, and clinician educator, Royal College of Physicians and Surgeons of Canada, Ottawa, Ontario, Canada; ORCID: https://orcid.org/0000-0003-1124-5786 .
  • Maxwell Spadafore
    Third-year medical student, University of Michigan Medical School, Ann Arbor, Michigan; maxspad@umich.edu; ORCID: http://orcid.org/0000-0001-5927-1428. Assistant dean for assessment, evaluation, and quality improvement and associate professor of internal medicine and learning health sciences, University of Michigan Medical School, Ann Arbor, Michigan; ORCID: http://orcid.org/0000-0002-3374-2989.
  • Stefanie S Sebok-Syer
    Department of Emergency Medicine, Stanford University School of Medicine, Stanford University, Palo Alto, CA, USA.
  • Brian C George
    B.C. George is assistant professor and director, Center for Surgical Training and Research, Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan.
  • Teresa M Chan
    T.M. Chan is associate professor, Division of Emergency Medicine, Department of Medicine, assistant dean, Program for Faculty Development, Faculty of Health Sciences, and adjunct scientist, McMaster Education Research, Innovation, and Theory (MERIT) program, McMaster University, Hamilton, Ontario, Canada; ORCID: https://orcid.org/0000-0001-6104-462X .
  • Andrew E Krumm
    Learning Health Sciences, Surgery, and Information, Medical School and School of Information, University of Michigan, Ann Arbor, Michigan, United States.

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