Appl Clin Inform
DOI: 10.1055/a-2657-8087
Research Article

The Influence of Artificial Intelligence Scribes on Clinician Experience and Efficiency Among Pediatric Subspecialists: A Rapid Randomized Quality Improvement Trial

H. Stella Shin
1   Information Services & Technology, Children’s Healthcare of Atlanta, Atlanta, United States
2   Department of Pediatrics, Emory University, Atlanta, United States (Ringgold ID: RIN1371)
,
Nikolay P Braykov
3   Pediatrics, Children's Healthcare of Atlanta Inc, Atlanta, United States (Ringgold ID: RIN1367)
,
Afrin Jahan
1   Information Services & Technology, Children’s Healthcare of Atlanta, Atlanta, United States
,
Jeremy Meller
1   Information Services & Technology, Children’s Healthcare of Atlanta, Atlanta, United States
,
Evan Orenstein
4   Pediatrics, Children's Healthcare of Atlanta Egleston Hospital, Atlanta, United States (Ringgold ID: RIN138610)
› Author Affiliations
Clinical Trial: Registration number (trial ID): NCT06812234, Trial registry: ClinicalTrials.gov (http://www.clinicaltrials.gov/), Type of Study: Randomized
Preview

Background: Artificial intelligence (AI) scribes may reduce documentation burden and improve clinician experience through generative AI automatically producing provider note sections from recordings of patient-provider encounters. Methods: We randomized pediatric subspecialty providers with ≥0.5 clinical Full Time Equivalent and stable electronic health record (EHR) log metrics to use Microsoft/Nuance Digital Ambient eXperience (DAX) Copilot from 5/1/2024 through 7/31/2024 (intervention group) or controls. Using difference-in-differences, we compared quantitative measures of subjective clinician experience using the KLAS Net EHR Experience survey, objective measures of clinician efficiency from EHR logs (e.g. pajama time), and business efficiency measures. At-the-elbow support checked in with intervention providers approximately weekly, and we assessed the sentiment of qualitative comments. Results: Twelve providers were randomized to the intervention and 10 to the control group. One intervention provider stopped using DAX due to ineffectiveness. In the intervention group, DAX was used to populate ≥1 character in 53% of visit notes (range across providers: 10.6% to 98.2%). Nine intervention and 7 control providers completed pre- and post-surveys. KLAS Net EHR Experience improved among intervention providers from 52.6 (70th percentile) to 75.2 (99th percentile) but dropped from 37.3 (38th percentile) to 30 (14th percentile) among control providers. Experiencing burnout dropped from 8 (89%) to 5 (56%) among intervention providers but remained stable at 3 (43%) in the control group. There was no significant change to pajama time (-9.4 minutes per scheduled day, 95% CI: -41.2 to +22.4), time in notes per encounter (+0.2 minutes per note, 95% CI: -6.6 to +6.9), or work Relative Value Units (wRVUs) per encounter (-0.03, 95% CI -0.5 to +0.44). Of 48 qualitative comments, 69% had a positive sentiment, 15% neutral, and 17% negative. Conclusion: Among pediatric subspecialists, AI scribes improved clinician experience and burnout without changing charting time or EHR work outside work hours.



Publication History

Received: 30 January 2025

Accepted after revision: 16 July 2025

Accepted Manuscript online:
17 July 2025

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