BRIEF REPORT
Karl T. Clebak, MD, MHA; Michael T. Partin, MD; Roland Newman, DO; Anthony Dambro, MD; Alyssa Anderson, MD; Erik Lehman, MS; Morris Taylor, MD; Misbah Keen, MD; Mack T. Ruffin, MD, MPH
Corresponding Author: Karl T. Clebak, MD, MHA; Department of Family and Community Medicine, The Pennsylvania State University, College of Medicine
Email: Kclebak@pennstatehealth.psu.edu
DOI: 10.3122/jabfm.2025.250003R1
Keywords: Artificial Intelligence, Chi-Square Test, Clinical Decision-Making, Diagnostic Techniques and Procedures, Family Medicine, Family Physicians, Health Personnel, Health Policy, Health Services, Information Systems, Information Technology, Medical Education, Medical Faculty, Medical Informatics, Organizational Innovation, Physician's Role, Primary Health Care, Technology
Dates: Submitted: 01-03-2025; Revised: 03-14-2025; Accepted: 03-31-2025
Status: In production for ahead of print.
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 was 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=0.023). 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 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.