From Burden to Balance: Trends in Documentation Workload with Ambient AI Scribe Adoption

BACKGROUND: Physician burnout, often driven by documentation burden in electronic health records (EHRs), remains a major challenge in clinical care. Ambient artificial intelligence (AI) scribes generate draft notes from clinical encounters, offering a scalable approach to reduce administrative workload, yet population-level evaluations remain limited. OBJECTIVES: To assess changes over time in clinician perceptions of workload, well-being, and care delivery following implementation of ambient AI scribes. METHODS: We conducted a prospective repeated cross-sectional survey study of physicians and advanced practice providers at four time points: baseline and approximately 1, 3, and 6 months post-implementation. Outcomes included cognitive workload (NASA Task Load Index), professional fulfillment and burnout (Professional Fulfillment Index and Mini-Z), and perceptions of documentation burden, communication, and clinical capacity. Responses were grouped into clinically meaningful categories, and chi-square tests compared distributions across time. Secondary analyses compared baseline characteristics of respondents with linkable data across timepoints to the full cohort and evaluated subgroup differences for specific items. RESULTS: 1,600 clinicians responded at baseline, with 172, 117, and 101 respondents at 1-, 3-, and 6-month follow-up, respectively. Over 95% of follow-up respondents reported generating ≥5 ambient AI-supported notes, with use increasing significantly over time (p < 0.05). Clinicians reported statistically significant reductions in mental demand, time pressure, and documentation effort at post-implementation intervals (p < 0.05). Time spent on documentation outside clinic declined, with more clinicians reporting <2 hours/day (p < 0.05). Burnout increased across follow-up timepoints (p < 0.05). Confidence in patient comprehension and willingness to increase patient volume improved. Subgroup analyses mirrored overall trends. CONCLUSIONS: Ambient AI scribes were associated with sustained reductions in cognitive workload and perceived documentation burden. Despite these improvements, burnout increased over time, likely reflecting multifactorial influences and limitations of unpaired survey data. These findings support continued implementation and further longitudinal evaluation of clinician well-being.

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Publication Details

Journal
Applied Clinical Informatics
Published
2026-06-12
DOI
https://doi.org/10.1055/a-2892-7988
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

From Burden to Balance: Trends in Documentation Workload with Ambient AI Scribe Adoption

Gary S. Fischer, L Giugale, Thomas Wojda, Jonathan Arnold et al.
Applied Clinical Informatics
Artificial Intelligence in Healthcare and Education
article

From Burden to Balance: Trends in Documentation Workload with Ambient AI Scribe Adoption

Gary S. Fischer, L Giugale, Thomas Wojda, Jonathan Arnold, Jennifer Berliner, Michael Curren
article en

Abstract

BACKGROUND: Physician burnout, often driven by documentation burden in electronic health records (EHRs), remains a major challenge in clinical care. Ambient artificial intelligence (AI) scribes generate draft notes from clinical encounters, offering a scalable approach to reduce administrative workload, yet population-level evaluations remain limited. OBJECTIVES: To assess changes over time in clinician perceptions of workload, well-being, and care delivery following implementation of ambient AI scribes. METHODS: We conducted a prospective repeated cross-sectional survey study of physicians and advanced practice providers at four time points: baseline and approximately 1, 3, and 6 months post-implementation. Outcomes included cognitive workload (NASA Task Load Index), professional fulfillment and burnout (Professional Fulfillment Index and Mini-Z), and perceptions of documentation burden, communication, and clinical capacity. Responses were grouped into clinically meaningful categories, and chi-square tests compared distributions across time. Secondary analyses compared baseline characteristics of respondents with linkable data across timepoints to the full cohort and evaluated subgroup differences for specific items. RESULTS: 1,600 clinicians responded at baseline, with 172, 117, and 101 respondents at 1-, 3-, and 6-month follow-up, respectively. Over 95% of follow-up respondents reported generating ≥5 ambient AI-supported notes, with use increasing significantly over time (p < 0.05). Clinicians reported statistically significant reductions in mental demand, time pressure, and documentation effort at post-implementation intervals (p < 0.05). Time spent on documentation outside clinic declined, with more clinicians reporting <2 hours/day (p < 0.05). Burnout increased across follow-up timepoints (p < 0.05). Confidence in patient comprehension and willingness to increase patient volume improved. Subgroup analyses mirrored overall trends. CONCLUSIONS: Ambient AI scribes were associated with sustained reductions in cognitive workload and perceived documentation burden. Despite these improvements, burnout increased over time, likely reflecting multifactorial influences and limitations of unpaired survey data. These findings support continued implementation and further longitudinal evaluation of clinician well-being.

Applied Clinical Informatics
University of Pittsburgh (US), UPMC Health System (US)
Openalex Percentile: Top 9%
Artificial Intelligence in Healthcare and Education
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