ArticleLearning health systems2025
Artificial intelligence and physician burnout: A productivity paradox.
Article in Learning health systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- How Does Medical Artificial Intelligence Revolutionize Physician Productivity?Yonsei medical journal · 2026Pooled it
- Generative AI Use, Perceived Usefulness, Perceived Risk, and Physician Burnout and Fulfillment Among Chinese Physicians: Mixed Methods Multiregional Study.Journal of medical Internet research · 2026Article
- Artificial Intelligence and Clinician Burnout in the United States: A Narrative Review.Cureus · 2026Review
- AI in Drug Discovery: Clinical Failures, Regulatory Reality, and the Validation Crisis Behind the Hype.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Real-World Data Needs Real-World Doctors: When Automation Advances Faster Than Clinical Workflow.Journal of participatory medicine · 2026Article
- Artificial intelligence in cardiology: a narrative review with focus on patient outcomes.Cardiovascular diagnosis and therapy · 2026Review
- Artificial intelligence and physician burnout: A productivity paradox.Learning health systems · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Introduction: Physician burnout persists in the American healthcare system. In part, this burnout is believed to be driven by the Electronic Health Record (EHR) and its fraught role in the clinical work of physicians. Artificial intelligence (AI)-enabled healthcare technologies are often promoted on the basis of their promise to reduce burnout by introducing efficiencies into clinical work, particularly related to EHR utilization and documentation. Where documentation is perceived as the problem, AI scribes are offered as the solution. Methods: This essay looks closely at existing studies of AI scribes in clinical context and draws upon experience and understanding of healthcare delivery and the EHR to anticipate how AI may related to provider burnout. Results: We find that it is premature to assert that AI tools will reduce physician burnout. Considering the integration of AI scribes into Learning Health Systems healthcare delivery becomes a starting point for understanding the challenges faced in safely adopting AI tools more generally, with attention to the healthcare workforce and patients. Conclusion: It is not a foregone conclusion that AI-enabled healthcare technologies, in their current state and application, will lead to improved healthcare delivery and reduced burnout. Instead, this is an open question that demands rigorous evaluation and high standards of evidence before we restructure the work of physicians and redefine the care of our patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.