Artificial intelligence (AI) has been increasingly integrated into various domains of healthcare, yet its application in psychotherapy remains underexplored. One of the most time-consuming tasks for psychotherapists is documentation, which can consume up to 20% of their working hours, contributing to burnout and reducing time spent on direct patient care. AI-powered tools designed to automate session note-taking offer a potential solution to this challenge. However, empirical evidence on their effectiveness is limited. The present study investigates the impact of Yung Sidekick, an AI-based documentation tool, on psychotherapists’ administrative workload, adherence to treatment plans, and perceived therapy progress. A randomized controlled trial was conducted with 70 licensed psychotherapists in the United States. Participants were randomly assigned to an experimental group using Yung Sidekick for one month or a control group maintaining standard documentation practices. Outcome measures included time spent on session notes and preparation, adherence to treatment plans, therapy progress, and professional well-being indicators. Results demonstrated that the experimental group showed significant reductions in time spent on session notes and preparation, as well as improvements in adherence to treatment plans and perceived therapy progress. However, no significant changes were observed in other well-being measures such as professional stress and burnout. These findings suggest that AI-assisted documentation can enhance efficiency and adherence to structured treatment approaches, but further research is needed to examine long-term effects and potential placebo influences. Implications for integrating AI into psychotherapy practice and future research directions are discussed.
References
[1]
Baumel, A., Muench, F., Edan, S., & Kane, J. M. (2019). Objective User Engagement with Mental Health Apps: Systematic Search and Panel-Based Usage Analysis. Journal of Medical Internet Research, 21, e14567. https://doi.org/10.2196/14567
[2]
Budd, J. (2023). Burnout Related to Electronic Health Record Use in Primary Care. Journal of Primary Care & Community Health, 14, 1-7. https://doi.org/10.1177/21501319231166921
[3]
Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A Global Measure of Perceived Stress. Journal of Health and Social Behavior, 24, 385-396. https://doi.org/10.2307/2136404
[4]
D’Alfonso, S. (2020). AI in Mental Health. Current Opinion in Psychology, 36, 112-117. https://doi.org/10.1016/j.copsyc.2020.04.005
[5]
Dawoodbhoy, F. M., Delaney, J., Cecula, P., Yu, J., Peacock, I., Tan, J. et al. (2021). AI in Patient Flow: Applications of Artificial Intelligence to Improve Patient Flow in NHS Acute Mental Health Inpatient Units. Heliyon, 7, e06993. https://doi.org/10.1016/j.heliyon.2021.e06993
[6]
Dymek, C., Kim, B., Melton, G. B., Payne, T. H., Singh, H., & Hsiao, C. (2021). Building the Evidence-Base to Reduce Electronic Health Record-Related Clinician Burden. Journal of the American Medical Informatics Association, 28, 1057-1061. https://doi.org/10.1093/jamia/ocaa238
[7]
Ebbers, T., Kool, R. B., Smeele, L. E., Dirven, R., den Besten, C. A., Karssemakers, L. H. E. et al. (2022). The Impact of Structured and Standardized Documentation on Documentation Quality; a Multicenter, Retrospective Study. Journal of Medical Systems, 46, Article No. 46. https://doi.org/10.1007/s10916-022-01837-9
[8]
Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., & Ranganath, R. (2020). A Review of Challenges and Opportunities in Machine Learning for Health. AMIA Joint Summits on Translational Science Proceedings, 2020, 191-200.
[9]
Gori, A., Topino, E., Brugnera, A., & Compare, A. (2022). Assessment of Professional Self‐efficacy in Psychological Interventions and Psychotherapy Sessions: Development of the Therapist Self-Efficacy Scale (T‐SES) and Its Application for Etherapy. Journal of Clinical Psychology, 78, 2122-2144. https://doi.org/10.1002/jclp.23391
[10]
Jacob, C., Sanchez-Vazquez, A., & Ivory, C. (2020). Social, Organizational, and Technological Factors Impacting Clinicians’ Adoption of Mobile Health Tools: Systematic Literature Review. JMIR mHealth and uHealth, 8, e15935. https://doi.org/10.2196/15935
[11]
Jensen-Doss, A., Haimes, E. M. B., Smith, A. M., Lyon, A. R., Lewis, C. C., Stanick, C. F. et al. (2018). Monitoring Treatment Progress and Providing Feedback Is Viewed Favorably but Rarely Used in Practice. Administration and Policy in Mental Health and Mental Health Services Research, 45, 48-61. https://doi.org/10.1007/s10488-016-0763-0
[12]
Lee, C., Britto, S., & Diwan, K. (2024). Evaluating the Impact of Artificial Intelligence (AI) on Clinical Documentation Efficiency and Accuracy across Clinical Settings: A Scoping Review. Cureus, 16, e73994. https://doi.org/10.7759/cureus.73994
[13]
Maslach, C., & Jackson, S. E. (1981). The Measurement of Experienced Burnout. Journal of Organizational Behavior, 2, 99-113. https://doi.org/10.1002/job.4030020205
[14]
Philippe, T. J., Sikder, N., Jackson, A., Koblanski, M. E., Liow, E., Pilarinos, A. et al. (2022). Digital Health Interventions for Delivery of Mental Health Care: Systematic and Comprehensive Meta-Review. JMIR Mental Health, 9, e35159. https://doi.org/10.2196/35159
[15]
Rollwage, M., Habicht, J., Juechems, K., Carrington, B., Viswanathan, S., Stylianou, M. et al. (2023). Using Conversational AI to Facilitate Mental Health Assessments and Improve Clinical Efficiency within Psychotherapy Services: Real-World Observational Study. JMIR AI, 2, e44358. https://doi.org/10.2196/44358
[16]
Stamm, B. H. (2010). The Concise ProQOL Manual (2nd ed.). ProQOL.org.
[17]
Zhang, M., Scandiffio, J., Younus, S., Jeyakumar, T., Karsan, I., Charow, R. et al. (2023). The Adoption of AI in Mental Health Care-Perspectives from Mental Health Professionals: Qualitative Descriptive Study. JMIR Formative Research, 7, e47847. https://doi.org/10.2196/47847
