Considering Clinician Competencies for the Implementation of Artificial Intelligence-Based Tools in Health Care: Findings From a Scoping Review

Kim V Garvey; Kelly Jean Thomas Craig; Regina Russell; Laurie L Novak; Don Moore; Bonnie M Miller

doi:10.2196/37478

Considering Clinician Competencies for the Implementation of Artificial Intelligence-Based Tools in Health Care: Findings From a Scoping Review

JMIR Med Inform. 2022 Nov 16;10(11):e37478. doi: 10.2196/37478.

Authors

Kim V Garvey^#^{1

2}, Kelly Jean Thomas Craig^#^{3

4}, Regina Russell⁵, Laurie L Novak^{6

7}, Don Moore⁵, Bonnie M Miller^{1

5}

Affiliations

¹ Center for Advanced Mobile Healthcare Learning, Vanderbilt University Medical Center, Nashville, TN, United States.
² Department of Anesthesiology, School of Medicine, Vanderbilt University, Nashville, TN, United States.
³ Center for Artificial Intelligence, Research, and Evaluation, IBM Watson Health, Cambridge, MA, United States.
⁴ Clinical Evidence Development, Aetna Medical Affairs, CVS Health, Hartford, CT, United States.
⁵ Department of Medical Education and Administration, School of Medicine, Vanderbilt University, Nashville, TN, United States.
⁶ Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, United States.
⁷ Center of Excellence in Applied Artificial Intelligence, Vanderbilt University Medical Center, Nashville, TN, United States.

^# Contributed equally.

PMID: 36318697
PMCID: PMC9713618
DOI: 10.2196/37478

Abstract

Background: The use of artificial intelligence (AI)-based tools in the care of individual patients and patient populations is rapidly expanding.

Objective: The aim of this paper is to systematically identify research on provider competencies needed for the use of AI in clinical settings.

Methods: A scoping review was conducted to identify articles published between January 1, 2009, and May 1, 2020, from MEDLINE, CINAHL, and the Cochrane Library databases, using search queries for terms related to health care professionals (eg, medical, nursing, and pharmacy) and their professional development in all phases of clinical education, AI-based tools in all settings of clinical practice, and professional education domains of competencies and performance. Limits were provided for English language, studies on humans with abstracts, and settings in the United States.

Results: The searches identified 3476 records, of which 4 met the inclusion criteria. These studies described the use of AI in clinical practice and measured at least one aspect of clinician competence. While many studies measured the performance of the AI-based tool, only 4 measured clinician performance in terms of the knowledge, skills, or attitudes needed to understand and effectively use the new tools being tested. These 4 articles primarily focused on the ability of AI to enhance patient care and clinical decision-making by improving information flow and display, specifically for physicians.

Conclusions: While many research studies were identified that investigate the potential effectiveness of using AI technologies in health care, very few address specific competencies that are needed by clinicians to use them effectively. This highlights a critical gap.

Keywords: artificial intelligence; clinical decision; clinical education; clinical tool; competency; decision-making; digital health; digital tool; educational framework; health care; health information; health technology; patient; physician.

©Kim V Garvey, Kelly Jean Thomas Craig, Regina Russell, Laurie L Novak, Don Moore, Bonnie M Miller. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 16.11.2022.

Publication types

Review