Enhancing Clinical Documentation Workflow with Ambient Artificial Intelligence: Clinician Perspectives on Work Burden, Burnout, and Job Satisfaction
Open accessAbstract
Objective: This study assessed the effects of an ambient artificial intelligence (AI) documentation platform on clinicians’ perceptions of documentation workflow. Materials and Methods: A pre- and post-implementation survey evaluated ambulatory clinician perceptions on impact of Abridge, an ambient AI documentation platform. Outcomes included clinical documentation burden, work after-hours, clinician burnout, work satisfaction, and patient access. Data were analyzed using descriptive statistics and proportional odds logistic regression to compare changes for concordant questions across pre- and post-surveys. Covariate analysis examined effect of specialty type and duration of use of the AI tool. Results: Survey response rates were 51.1% (94/181) pre-implementation and 75.9% (101/133) post-implementation. Clinician perception of ease of documentation workflow (OR = 6.91, 95% CI: 3.90 to 12.56, p<0.001) and in completing notes associated with usage of the AI tool (OR = 4.95, 95% CI: 2.87 to 8.69, p<0.001) was significantly improved. The majority of respondents agreed that the AI tool decreased documentation burden, decreased the time spent documenting outside clinical hours, reduced burnout risk, and increased job satisfaction, with 48% agreeing that an additional patient could be seen if needed. Clinician specialty type and number of days using the AI tool did not significantly affect survey responses. Discussion: Clinician experience and efficiency was dramatically improved with use of Abridge across a breadth of specialties. Conclusion: An ambient AI documentation platform had tremendous impact on improving clinician experience within a short time frame. Future studies should utilize validated instruments for clinician efficiency and burnout and compare impact across AI platforms.
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Our respondents reported higher levels of satisfaction than previous research.
“although our respondents reported higher levels of satisfaction.9”
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Evaluation of an Ambient Artificial Intelligence Documentation Platform for Clinicians
Stults, C. D., Deng, S., Martinez, M. C., Wilcox, J., Szwerinski, N., Chen, K. H., Driscoll, S., Washburn, J., & Jones, V. G. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.8614 · Cited by 147 · see citing works
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- Authors
- Albrecht, M., Shanks, D., Shah, T., Hudson, T., Thompson, J., Filardi, T., Wright, K., Ator, G., & Smith, T. R.
- Affiliations
- University of Kansas Medical Center (US)
- The University of Kansas Health System (US)
- Corresponding author
- Albrecht, Michael
- Venue
- medRxiv
- Publisher
- openRxiv
- Published
- 2024-08-13
- OpenAlex
- W4401537157
- Cited by
- 11
- References
- 13
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- medRxiv (acceptedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Keywords
- clinical documentation, clinician burnout, job satisfaction, documentation burden, outpatient care, patient access, clinician experience
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