Competencies for the Use of Artificial Intelligence-Based Tools by Health Care Professionals

Regina G Russell; Laurie Lovett Novak; Mehool Patel; Kim V Garvey; Kelly Jean Thomas Craig; Gretchen P Jackson; Don Moore; Bonnie M Miller

doi:10.1097/ACM.0000000000004963

Competencies for the Use of Artificial Intelligence-Based Tools by Health Care Professionals

Acad Med. 2023 Mar 1;98(3):348-356. doi: 10.1097/ACM.0000000000004963. Epub 2022 Sep 6.

Authors

Regina G Russell¹, Laurie Lovett Novak², Mehool Patel³, Kim V Garvey⁴, Kelly Jean Thomas Craig⁵, Gretchen P Jackson⁶, Don Moore⁷, Bonnie M Miller⁸

Affiliations

¹ R.G. Russell is director of learning system outcomes, Office of Undergraduate Medical Education, and assistant professor of medical education and administration, Vanderbilt University School of Medicine, Nashville Tennessee; ORCID: https://orcid.org/0000-0002-5540-7073 .
² L.L. Novak is director, Center of Excellence in Applied Artificial Intelligence, Vanderbilt University Medical Center, and associate professor of biomedical informatics, Vanderbilt University School of Medicine, Nashville, Tennessee; ORCID: https://orcid.org/0000-0002-0415-4301 .
³ M. Patel is associate chief health officer and chief medical officer of provider analytics, IBM Watson Health, Cambridge, Massachusetts, and clinical professor, Northeast Ohio Medical University, Rootstown, Ohio.
⁴ K.V. Garvey is research instructor in anesthesiology, Vanderbilt University School of Medicine, and director of operations, Center for Advanced Mobile Healthcare Learning, Vanderbilt University Medical Center, Nashville, Tennessee; ORCID: https://orcid.org/0000-0002-2427-0182 .
⁵ K.J.T. Craig is lead director, Clinical Evidence Development, Aetna Medical Affairs, CVS Health. At the time this work was completed, the author was deputy chief science officer of evidence-based practice, Center for AI, Research, and Evaluation, IBM Watson Health, Cambridge, Massachusetts; ORCID: https://orcid.org/0000-0002-9954-2795 .
⁶ G.P. Jackson is vice president and scientific medical officer, Intuitive Surgical, Sunnyvale, California, and associate professor of surgery, pediatrics, and biomedical informatics, Vanderbilt University School of Medicine, Nashville, Tennessee. At the beginning of this work, the author was vice president and chief science officer, IBM Watson Health, Cambridge, Massachusetts; ORCID: https://orcid.org/0000-0002-3242-8058 .
⁷ D. Moore is emeritus professor of medical education and administration, Vanderbilt University School of Medicine, Nashville, Tennessee.
⁸ B.M. Miller is professor of medical education and administration, Vanderbilt University School of Medicine, and director, Center for Advanced Mobile Healthcare Learning, Vanderbilt University Medical Center, Nashville, Tennessee; ORCID: https://orcid.org/0000-0002-7333-3389 .

PMID: 36731054
DOI: 10.1097/ACM.0000000000004963

Abstract

Purpose: The expanded use of clinical tools that incorporate artificial intelligence (AI) methods has generated calls for specific competencies for effective and ethical use. This qualitative study used expert interviews to define AI-related clinical competencies for health care professionals.

Method: In 2021, a multidisciplinary team interviewed 15 experts in the use of AI-based tools in health care settings about the clinical competencies health care professionals need to work effectively with such tools. Transcripts of the semistructured interviews were coded and thematically analyzed. Draft competency statements were developed and provided to the experts for feedback. The competencies were finalized using a consensus process across the research team.

Results: Six competency domain statements and 25 subcompetencies were formulated from the thematic analysis. The competency domain statements are: (1) basic knowledge of AI: explain what AI is and describe its health care applications; (2) social and ethical implications of AI: explain how social, economic, and political systems influence AI-based tools and how these relationships impact justice, equity, and ethics; (3) AI-enhanced clinical encounters: carry out AI-enhanced clinical encounters that integrate diverse sources of information in creating patient-centered care plans; (4) evidence-based evaluation of AI-based tools: evaluate the quality, accuracy, safety, contextual appropriateness, and biases of AI-based tools and their underlying data sets in providing care to patients and populations; (5) workflow analysis for AI-based tools: analyze and adapt to changes in teams, roles, responsibilities, and workflows resulting from implementation of AI-based tools; and (6) practice-based learning and improvement regarding AI-based tools: participate in continuing professional development and practice-based improvement activities related to use of AI tools in health care.

Conclusions: The 6 clinical competencies identified can be used to guide future teaching and learning programs to maximize the potential benefits of AI-based tools and diminish potential harms.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence*
Clinical Competence
Delivery of Health Care
Health Personnel
Humans
Learning*