Beyond mathematics, statistics, and programming: data science, machine learning, and artificial intelligence competencies and curricula for clinicians, informaticians, science journalists, and researchers

William R Hersh; Robert E Hoyt; Steven Chamberlin; Jessica S Ancker; Aditi Gupta; Tara B Borlawsky-Payne

doi:10.1080/20476965.2023.2237745

Beyond mathematics, statistics, and programming: data science, machine learning, and artificial intelligence competencies and curricula for clinicians, informaticians, science journalists, and researchers

Health Syst (Basingstoke). 2023 Jul 18;12(3):255-263. doi: 10.1080/20476965.2023.2237745. eCollection 2023.

Authors

William R Hersh¹, Robert E Hoyt², Steven Chamberlin¹, Jessica S Ancker³, Aditi Gupta⁴, Tara B Borlawsky-Payne⁴

Affiliations

¹ Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.
² Department of Medicine, Virginia Commonwealth University, Richmond, VA, USA.
³ Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
⁴ Institute for Informatics, Washington University, St. Louis, MO, USA.

PMID: 37860593
PMCID: PMC10583607 (available on 2024-07-18)
DOI: 10.1080/20476965.2023.2237745

Abstract

Data science, machine learning and artificial intelligence applications impact clinicians, informaticians, science journalists, and researchers. Most biomedical data science training focuses on learning a programming language in addition to higher mathematics and advanced statistics. This approach is appropriate for graduate students but greatly reduces the number of individuals in healthcare who can be involved in data science. To serve these four stakeholder audiences, we describe several curricular strategies focusing on solving real problems of interest to these audiences. Relevant competencies for these audiences include using intuitive programming tools that facilitate data exploration with minimal programming background, creating data models, evaluating results of data analyses, and assessing data science research reports, among others. Offering the curricula described here more broadly could broaden the stakeholder groups knowledgeable about and engaged in data science.

Keywords: Data science; artificial intelligence; biomedical research; clinicians; health and clinical informatics; machine learning.