Other Titles
The Effect of Artificial Intelligence Scribes on Provider Burnout Scores [Poster Title]
Abstract
Background: Burnout is on the rise and contributes to critical patient care errors, poor patient perception of care, and a reduction in provider life satisfaction1,2. Some primary causes of burnout among healthcare providers are the escalating administrative burdens and extensive screen time associated with electronic health records (EHR) and other documentation tasks3. AI-assisted charting has shown promising results in improving provider satisfaction, increasing time with patients, reducing administrative workload, reducing EHR interface time, increasing patient satisfaction, and reducing provider self-reports of fatigue and dread5.
Purpose: This project aims to examine the effect of the AI-assisted scribe technology on provider burnout in an inpatient and an outpatient mental healthcare care facility over the course of a month.
Methods: Utilizing the ADKAR model of change, the project offers a structured adoption of new technology. Mobius Conveyer AI scribe will be deployed in a mental healthcare setting with voice-to-text capabilities and automated encounter summarization, allowing providers to capture therapeutic interactions in a HIPAA-compliant and encrypted manner. A quasi-experimental design will assess the impact of the AI-assisted documentation, evaluating provider burnout before and after the intervention using the Maslach Burnout Inventory (MBI). Additional measures include onboarding surveys to assess the demographic and technological comfortability of each participant.
Outcomes: Data from the MBI and onboarding surveys will undergo statistical analysis to compare pre- and post-intervention scores, with descriptive statistics summarizing demographic information, baseline characteristics, and feasibility data.
Implications: The successful adoption of AI-assisted documentation could transform DNP practice by streamlining workflows and improving the quality of patient care. It is anticipated that the use of the AI scribe to generate automated clinic notes will decrease administrative workload and reduce provider burnout.
Notes
References:
1. Nigam, J. A. (2023). Vital signs: health worker–perceived working conditions and symptoms of poor mental health—Quality of Worklife Survey, United States, 2018–2022. MMWR. Morbidity and Mortality Weekly Report, 72. https://www.cdc.gov/vitalsigns/health-worker-mental-health/index.html
2. Quan, C. E., Bustos Sanabria, G. J., Culumber, J., Hammond, R. W., & Sanchez-Gonzalez, M. A. (2024). Impact of provider burnout on the quality of healthcare services: A brief review. Journal of Preventive and Complementary Medicine, 3(2), 103-109. 9 https://doi.org/10.22034/jpcm.2024.468645.1175
3. Singh, R., Volner, K., & Marlowe, D. (2024). Provider Burnout. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK538330/
4. Benko, S., Idarraga, A. J., Bohl, D. D., & Hamid, K. S. (2022). Virtual scribe services decrease documentation burden without affecting patient satisfaction: a randomized controlled trial. Foot & Ankle Specialist, 15(3), 252-257. https://doi.org/10.1177/1938640020950544
5. Stephens, J., Kieber-Emmons, A. M., Johnson, M., & Greenberg, G. M. (2022). Implementation of a Virtual Asynchronous Scribe Program to Reduce Physician Burnout. Journal of Healthcare Management / American College of Healthcare Executives, 67(6), 425–435. https://doi.org/10.1097/JHM-D-21-00329
Sigma Membership
Psi at-Large
Type
Poster
Format Type
Text-based Document
Study Design/Type
Other
Research Approach
Other
Keywords:
Academic-clinical Partnership, Stress and Coping, Burnout, Implementation Science
Recommended Citation
Boland, Dillon and Harvey, Kayla, "Addressing Provider Burnout with AI Scribe Technology in Mental Health Practice Settings" (2025). International Nursing Research Congress (INRC). 103.
https://www.sigmarepository.org/inrc/2025/posters_2025/103
Conference Name
36th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Seattle, Washington, USA
Conference Year
2025
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Addressing Provider Burnout with AI Scribe Technology in Mental Health Practice Settings
Seattle, Washington, USA
Background: Burnout is on the rise and contributes to critical patient care errors, poor patient perception of care, and a reduction in provider life satisfaction1,2. Some primary causes of burnout among healthcare providers are the escalating administrative burdens and extensive screen time associated with electronic health records (EHR) and other documentation tasks3. AI-assisted charting has shown promising results in improving provider satisfaction, increasing time with patients, reducing administrative workload, reducing EHR interface time, increasing patient satisfaction, and reducing provider self-reports of fatigue and dread5.
Purpose: This project aims to examine the effect of the AI-assisted scribe technology on provider burnout in an inpatient and an outpatient mental healthcare care facility over the course of a month.
Methods: Utilizing the ADKAR model of change, the project offers a structured adoption of new technology. Mobius Conveyer AI scribe will be deployed in a mental healthcare setting with voice-to-text capabilities and automated encounter summarization, allowing providers to capture therapeutic interactions in a HIPAA-compliant and encrypted manner. A quasi-experimental design will assess the impact of the AI-assisted documentation, evaluating provider burnout before and after the intervention using the Maslach Burnout Inventory (MBI). Additional measures include onboarding surveys to assess the demographic and technological comfortability of each participant.
Outcomes: Data from the MBI and onboarding surveys will undergo statistical analysis to compare pre- and post-intervention scores, with descriptive statistics summarizing demographic information, baseline characteristics, and feasibility data.
Implications: The successful adoption of AI-assisted documentation could transform DNP practice by streamlining workflows and improving the quality of patient care. It is anticipated that the use of the AI scribe to generate automated clinic notes will decrease administrative workload and reduce provider burnout.
Description
Burnout is on the rise and contributes to patient care errors, poor patient perception of care, and reduction in provider satisfaction. AI-assisted charting has shown promising results in improving provider satisfaction, increasing time with patients and reducing administrative workload. This project aims to examine the effect of the AI-assisted scribe technology on provider burnout in mental healthcare.