RT Journal Article
SR Electronic
T1 Artificial Intelligence (AI) Adoption, Policies, and Goals in Family Medicine: A Survey of Department Chairs
JF The Journal of the American Board of Family
                Medicine
JO J Am Board Fam Med
FD American Board of Family Medicine
DO 10.3122/jabfm.2025.250003R1
A1 Clebak, Karl T.
A1 Partin, Michael T.
A1 Newman, Roland
A1 Dambro, Anthony
A1 Anderson, Alyssa
A1 Lehman, Erik
A1 Taylor, Morris
A1 Keen, Misbah
A1 Ruffin, Mack T.
YR 2025
UL http://www.jabfm.org/content/early/2025/10/17/jabfm.2025.250003R1.abstract
AB Background: Artificial Intelligence (AI) has the potential to reshape family medicine by enhancing clinical, educational, administrative, and research operations. Despite AI's transformative potential, its adoption is inconsistent, and strategic frameworks remain limited. This study explores current AI adoption, organizational policies, integration priorities, and budget allocations within family medicine departments.Methods: A survey of 218 family medicine department chairs in the US and Canada was conducted via SurveyMonkey from August 13 to September 20, 2024, as part of the Council of Academic Family Medicine (CAFM) Educational Research Alliance (CERA) omnibus project. Survey questions assessed current and planned AI utilization, presence of formal departmental or organizational policies (defined as written guidelines, strategic plans, or frameworks), integration priorities, and budget allocations. Data were analyzed using Chi-square tests, Wilcoxon Rank Sum tests, and Kruskal-Wallis tests, with a primary focus on bivariate comparisons.Results: The survey achieved a 50.9% response rate (111/218). Current AI use was reported by 56.9% (62/109), while 37.6% (41/109) indicated formal organizational policies. Primary goals for AI integration included improving clinical operations (52.3%), administrative streamlining (16.5%), educational applications (11.9%), and research (4.6%). Budget allocations were minimal (median, 0%; mean 2.4%), though departmental budgets likely underestimate actual institutional investment in AI. Departments reporting AI use had significantly more full-time equivalent faculty (median, 40.0 vs 25.5, P = .023). Geographic and chair demographics were not significantly associated with differences in AI adoption.Conclusions: AI integration in family medicine departments is viewed as essential, though current adoption is limited by uncertain strategic planning and minimal departmental budget allocations, potentially reflecting reliance on centralized institutional information technology (IT) investments. While AI is widely viewed as important, structured policy frameworks and implementation strategies are still developing. Further research is essential to guide policy development and strategic investment to ensure AI’s safe, efficient, and effective integration into family medicine.