Evidence map›Paper›PMID 41115753›Full record

ArticleJournal of the American Board of Family Medicine : JABFM2025

Artificial Intelligence (AI) Adoption, Policies, and Goals in Family Medicine: A Survey of Department Chairs.

Karl T Clebak, Michael T Partin, Roland Newman, Anthony Dambro, Alyssa Anderson, Erik Lehman, Morris Taylor, Misbah Keen, Mack T Ruffin

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In one paragraph

Article in Journal of the American Board of Family Medicine : JABFM, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Karl T ClebakFrom the Department of Family and Community Medicine, The Pennsylvania State University, College of Medicine (KTC, MTP, RN, AD, AA, MT, MTR); Department of Public Health Sciences, The Pennsylvania State University, College of Medicine (EL); Department of Family Medicine, University of Washington (MK). Kclebak@pennstatehealth.psu.edu.
Michael T PartinFrom the Department of Family and Community Medicine, The Pennsylvania State University, College of Medicine (KTC, MTP, RN, AD, AA, MT, MTR); Department of Public Health Sciences, The Pennsylvania State University, College of Medicine (EL); Department of Family Medicine, University of Washington (MK).
Roland NewmanFrom the Department of Family and Community Medicine, The Pennsylvania State University, College of Medicine (KTC, MTP, RN, AD, AA, MT, MTR); Department of Public Health Sciences, The Pennsylvania State University, College of Medicine (EL); Department of Family Medicine, University of Washington (MK).
Anthony DambroFrom the Department of Family and Community Medicine, The Pennsylvania State University, College of Medicine (KTC, MTP, RN, AD, AA, MT, MTR); Department of Public Health Sciences, The Pennsylvania State University, College of Medicine (EL); Department of Family Medicine, University of Washington (MK).
Alyssa AndersonFrom the Department of Family and Community Medicine, The Pennsylvania State University, College of Medicine (KTC, MTP, RN, AD, AA, MT, MTR); Department of Public Health Sciences, The Pennsylvania State University, College of Medicine (EL); Department of Family Medicine, University of Washington (MK).
Erik LehmanFrom the Department of Family and Community Medicine, The Pennsylvania State University, College of Medicine (KTC, MTP, RN, AD, AA, MT, MTR); Department of Public Health Sciences, The Pennsylvania State University, College of Medicine (EL); Department of Family Medicine, University of Washington (MK).
Morris TaylorFrom the Department of Family and Community Medicine, The Pennsylvania State University, College of Medicine (KTC, MTP, RN, AD, AA, MT, MTR); Department of Public Health Sciences, The Pennsylvania State University, College of Medicine (EL); Department of Family Medicine, University of Washington (MK).
Misbah KeenFrom the Department of Family and Community Medicine, The Pennsylvania State University, College of Medicine (KTC, MTP, RN, AD, AA, MT, MTR); Department of Public Health Sciences, The Pennsylvania State University, College of Medicine (EL); Department of Family Medicine, University of Washington (MK).
Mack T RuffinFrom the Department of Family and Community Medicine, The Pennsylvania State University, College of Medicine (KTC, MTP, RN, AD, AA, MT, MTR); Department of Public Health Sciences, The Pennsylvania State University, College of Medicine (EL); Department of Family Medicine, University of Washington (MK).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial 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.

methodsA 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.

resultsThe 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,

conclusionsAI 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.

Indexed as

Artificial IntelligenceFamily PracticeOrganizational PolicyBudgetsCanadaFaculty, MedicalHumansOrganizational ObjectivesSurveys and QuestionnairesUnited StatesArtificial IntelligenceChi-Square TestClinical Decision-MakingDiagnostic Techniques and ProceduresFamily MedicineFamily PhysiciansHealth PersonnelHealth PolicyHealth ServicesInformation SystemsInformation TechnologyMedical EducationMedical FacultyMedical InformaticsOrganizational InnovationPhysician's RolePrimary Health CareTechnology

Identifiers

PMID41115753
PMCPMC12978605

What Socratic holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.