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Pre-Post Evaluation of Documentation Burden, Time Perception, and Cognitive Workload of Ambient AI Scribe Tools
Leopold Arko1, Tony Huy Nguyen1, Michelle Stoffel1,2
1University of Minnesota, Minneapolis, MN, USA.
Summary
Ambient AI scribes significantly reduce clinician-perceived documentation burden and cognitive workload, improving burnout metrics. Objective documentation time remained stable, suggesting AI scribes enhance perceived efficiency and workload calibration.
Area of Science:
- Medical Informatics
- Clinical Practice Improvement
- Artificial Intelligence in Healthcare
Background:
- Clinician burnout is exacerbated by documentation burden and cognitive load.
- Ambient AI scribes present a potential solution, but real-world data is scarce.
Purpose of the Study:
- To evaluate the impact of an ambient AI scribe on clinician documentation burden, cognitive workload, and burnout.
- To assess the alignment between perceived and objective documentation time.
Main Methods:
- A 60-day pilot study involving 40 clinicians using an ambient AI scribe.
- Pre- and post-implementation surveys assessing burnout, workload (NASA-TLX), and workflow.
- Analysis of electronic health record (EHR)-autologged after-hours documentation data.
Main Results:
- Clinicians reported significant reductions in perceived documentation time and after-hours work (p < 0.001).
- Significant decreases were observed in NASA-TLX domains, including mental demand, effort, and stress (p ≤0.001).
- Objective EHR documentation minutes did not change significantly (p = 0.15).
Conclusions:
- Ambient AI scribes effectively reduce perceived workload and improve the calibration of documentation burden.
- AI scribes enhance the clinician experience and perceived efficiency, even if objective time metrics remain unchanged.
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