The role of data science and machine learning in Health Professions Education: practical applications, theoretical contr
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The role of data science and machine learning in Health Professions Education: practical applications, theoretical contributions, and epistemic beliefs Martin G. Tolsgaard1,2 · Christy K. Boscardin3 · Yoon Soo Park4,5 · Monica M. Cuddy6 · Stefanie S. Sebok‑Syer7 Received: 4 August 2020 / Accepted: 24 October 2020 © Springer Nature B.V. 2020
Abstract Data science is an inter-disciplinary field that uses computer-based algorithms and methods to gain insights from large and often complex datasets. Data science, which includes Artificial Intelligence techniques such as Machine Learning (ML), has been credited with the promise to transform Health Professions Education (HPE) by offering approaches to handle big (and often messy) data. To examine this promise, we conducted a critical review to explore: (1) published applications of data science and ML in HPE literature and (2) the potential role of data science and ML in shifting theoretical and epistemological perspectives in HPE research and practice. Existing data science studies in HPE are often not informed by theory, but rather oriented towards developing applications for specific problems, uses, and contexts. The most common areas currently being studied are procedural (e.g., computer-based tutoring or adaptive systems and assessment of technical skills). We found that epistemic beliefs informing the use of data science and ML in HPE poses a challenge for existing views on what constitutes objective knowledge and the role of human subjectivity for instruction and assessment. As a result, criticisms have emerged that the integration of data science in the field of HPE is in danger of becoming technically driven and narrowly focused in its approach to teaching, learning and assessment. Our findings suggest that researchers tend to formalize around the epistemological stance driven largely by traditions of a research paradigm. Future data science studies in HPE need to involve both education scientists and data scientists to ensure mutual advancements in the development of educational theory and practical applications. This may be one of the most important tasks in the integration of data science and ML in HPE research in the years to come. Keywords Research in Health Professions Education · Medical education research · data science · Machine learning · Artificial intelligence
* Martin G. Tolsgaard [email protected] Extended author information available on the last page of the article
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Introduction Almost a decade ago, Geoff Norman wrote a piece entitled, “Medicine man meets machine” where he editorialized the commercial success of Watson, arguing that Watson was a demonstration of how Artificial Intelligence (AI) may influence medicine in the future (Norman 2011). Since then, a growing body of scholarship in health professions education (HPE) has touted the potential for AI, rooted in the field of data science, to revolutionize instruction, learning, and assessment (Masters 2019; Chahine et al. 2018; Mur
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