Work

Online survey assessing US primary care physicians’ attitudes toward AI use in clinical administrative tasks

Year: 2026

Type: article

Abstract: OBJECTIVES: To examine primary care physicians' attitudes regarding artificial intelligence (AI) use for administrative clinical tasks. METHODS: Web-based survey with US physicians in family medicine or internal medicine (N=420, response rate 5.13%). Two hypothetical AI tools for administrative clinical activities were described. We examined physicians' attitudes towards AI tools, and their associations with practice years, exposure to AI, use case and stakeholder type were evaluated using generalised estimating equations. RESULTS: Participants were on average 49.6 years (SD=12.5) and 56.7% men (238/420). Physicians with fewer practice years were more likely to endorse the tools' benefits (OR 1.70-1.96), the tools' benefits outweighing risks (OR 1.79-2.06) and their openness to use (OR 1.63-1.83), and were less likely to endorse disclosure of AI use (OR 0.60 (95% CI 0.36 to 0.998)). Physicians with AI exposure were more likely to agree the tools' benefits outweighed their risks (OR 1.51 (95% CI 1.06 to 2.16)). Physicians were more likely to endorse the tools' benefit to physicians (OR 4.94 (95% CI 4.16 to 5.86)) and physicians' openness to using them (OR 3.53 (95% CI 2.97 to 4.20)) than they were to endorse their benefit to patients and patients' openness. Physicians rated an AI tool for notes generation as more beneficial than one for billing assistance (OR 1.73 (95% CI 1.39 to 2.16)). DISCUSSION: Although the findings are preliminary, US primary care physicians' attitudes toward AI for clinical administration varied by practice years, prior exposure to AI, use case and stakeholder type. CONCLUSION: Our findings highlight opportunities to develop training and implementation strategies in service of advancing safe and effective integration of administrative AI tools in primary care.

Source: BMJ Health & Care Informatics

DOI: 10.1136/bmjhci-2024-101400

Authors: Bohye Kim, Katie Ryan, Max Kasun, Laura Weiss Roberts, Jane Kim

Institutions: Stanford Medicine, Stanford University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,207