Enhancing clinical documentation with ambient artificial intelligence: a quality improvement survey assessing clinician perspectives on work burden, burnout, and job satisfaction
Michael Albrecht, Denton Shanks, Tina Shah, Taina Hudson, Jeffrey Thompson, Tanya Filardi, Kelli Wright, Gregory A Ator, Timothy Ryan SmithAbstract
Objective
This study evaluates the impact of an ambient artificial intelligence (AI) documentation platform on clinicians’ perceptions of documentation workflow.
Materials and Methods
An anonymous pre- and non-anonymous 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, and work satisfaction. 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 AI tool usage.
Results
Survey response rates were 51.9% (93/181) pre-implementation and 74.4% (99/133) post-implementation. Clinician perception of ease of documentation workflow (OR = 6.91, 95% CI: 3.90-12.56, P <.001) and in completing notes associated with usage of the AI tool (OR = 4.95, 95% CI: 2.87-8.69, P <.001) was significantly improved. Most 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 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.