Impact of artificial intelligence on electronic health record-related burnouts among healthcare professionals: systematic review
Open accessAbstract
Introduction: The implementation of electronic health records (EHRs) has revolutionized modern clinical practice, increasing efficiency, accessibility, and quality of care. Nevertheless, EHR-related workload has been considered as a significant contributor to healthcare professionals' burnout, a syndrome associated with emotional exhaustion, depersonalization, and reduced personal accomplishment. As modern health system explores technological solutions, artificial intelligence (AI) has gained attention for its potential to facilitate documentation processes and alleviate cognitive burden. This systematic review aims to explore and understand the impact of artificial intelligence on burnout associated with electronic health records among healthcare professionals. Methods: A systematic literature review was conducted following the PRISMA 2020 guidelines. Relevant studies published between 2019 and 2025 were retrieved from three electronic databases: PubMed, Scopus, and Web of Science. The search strategy included three main domains: artificial intelligence, electronic health records, and healthcare professional burnout. Eligible included studies are peer-reviewed original research articles that evaluated the impact of AI-based technologies on burnout among healthcare professionals. The screening and selection processes were carried out by following the PRISMA framework. Methodological quality assessment of the included studies was performed using the Joanna Briggs Institute Critical Appraisal Tools. Results: Of the 287 records initially identified, eight studies met the inclusion criteria. The majority of identified studies were conducted in the United States and Canada. The identified interventions were categorized into four domains: ambient artificial intelligence scribes, clinical decision support systems, large language models, and natural language processing tools. Most studies focused on mitigating documentation or inbox-related burdens and reported positive outcomes, including decreased documentation time, enhanced workflow efficiency, and reduced symptoms of burnout among healthcare professionals. Nonetheless, several methodological limitations were observed, including the absence of control groups, small sample sizes, and short follow-up periods, which constrain the generalizability of the findings. Discussion: The integration of artificial intelligence into electronic health record systems may have potential to alleviate documentation burden and inbox management burden. Although preliminary findings are promising, further methodologically robust research is necessary to evaluate long-term outcomes, assess usability across diverse clinical contexts, and ensure the safe and effective implementation of AI technologies in routine healthcare practice. Systematic review registration: https://osf.io/pevfj.
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Ambient AI has shown promise in reducing time spent in documentation and improving healthcare practitioner well-being and communication with patients.
“Ambient AI has been used more widely for healthcare practitioner workflows, showing promise in reducing time spent in documentation, improving healthcare practitioner well-being and communication with patients [12].”
From Protocol for an EHR-embedded pragmatic randomized control trial of Ambient AI to Reduce Nursing Staff Documentation Time (2026) · cites this paper as [12]
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Cites
The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283 · see citing works
Understanding the burnout experience: Recent research and its implications for psychiatry
Maslach, C., & Leiter, M. P. (2016) · World Psychiatry · doi:10.1002/wps.20311 · Cited by 4,200 · see citing works
Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146 · see citing works
Methodological quality of case series studies: an introduction to the JBI critical appraisal tool
Munn, Z., Barker, T. H., Moola, S., Tufanaru, C., Stern, C., McArthur, A., Stephenson, M., & Aromataris, E. (2019) · JBI Database of Systematic Reviews and Implementation Reports · doi:10.11124/jbisrir-d-19-00099 · Cited by 1,581 · see citing works
Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties
Sinsky, C., Colligan, L., Li, L., Prgomet, M., Reynolds, S., Goeders, L., Westbrook, J., Tutty, M., & Blike, G. (2016) · Annals of Internal Medicine · doi:10.7326/m16-0961 · Cited by 1,494 · see citing works
Arndt, B. G., Beasley, J. W., Watkinson, M. D., Temte, J. L., Tuan, W.-J., Sinsky, C. A., & Gilchrist, V. J. (2017) · The Annals of Family Medicine · doi:10.1370/afm.2121 · Cited by 972 · see citing works
The impact of artificial intelligence in medicine on the future role of the physician
Ahuja, A. S. (2019) · PeerJ · doi:10.7717/peerj.7702 · Cited by 789 · see citing works
AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications
Rashid, A. B., & Kausik, M. A. K. (2024) · Hybrid Advances · doi:10.1016/j.hybadv.2024.100277 · Cited by 764 · see citing works
Physician stress and burnout: the impact of health information technology
Gardner, R. L., Cooper, E., Haskell, J., Harris, D. A., Poplau, S., Kroth, P. J., & Linzer, M. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy145 · Cited by 566 · see citing works
Security and privacy of electronic health records: Concerns and challenges
Keshta, I., & Odeh, A. (2021) · Egyptian Informatics Journal · doi:10.1016/j.eij.2020.07.003 · Cited by 498 · see citing works
Chapter 3: Systematic Reviews of Effectiveness
Tufănaru, C., Munn, Z., Aromataris, E., Campbell, J. M., & Hopp, L. J. (2019) · JBI eBooks · doi:10.46658/jbirm-17-03 · Cited by 449 · see citing works
Role of artificial intelligence in patient safety outcomes: systematic literature review
Choudhury, A., & Asan, O. (2020) · JMIR Medical Informatics · doi:10.2196/18599 · Cited by 440 · see citing works
Burnout and health care workforce turnover
Willard-Grace, R., Knox, M., Huang, B., Hammer, H., Kivlahan, C., & Grumbach, K. (2019) · The Annals of Family Medicine · doi:10.1370/afm.2338 · Cited by 400 · see citing works
Artificial Intelligence–Generated Draft Replies to Patient Inbox Messages
Garcia, P., Ma, S. P., Shah, S. J., Smith, M., Jeong, Y., Devon-Sand, A., Tai-Seale, M., Takazawa, K., Clutter, D., Vogt, K., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.3201 · Cited by 257 · see citing works
Impact of patient access to their electronic health record: systematic review
Tapuria, A., Porat, T., Kalra, D., D'souza, G., Sun, X., & Ćurčin, V. (2021) · Informatics for Health and Social Care · doi:10.1080/17538157.2021.1879810 · Cited by 232 · see citing works
Shah, S. J., Devon-Sand, A., Ma, S. P., Jeong, Y., Crowell, T., Smith, M., Liang, A. S., Delahaie, C., Hsia, C., Shanafelt, T., et al. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae295 · Cited by 224 · see citing works
Burnout Related to Electronic Health Record Use in Primary Care
Budd, J. (2023) · Journal of Primary Care & Community Health · doi:10.1177/21501319231166921 · Cited by 223 · see citing works
Beyond Burnout — Redesigning Care to Restore Meaning and Sanity for Physicians
Wright, A. A., & Katz, I. T. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1716845 · Cited by 212 · see citing works
Artificial intelligence: opportunities and implications for the health workforce
Hazarika, I. (2020) · International Health · doi:10.1093/inthealth/ihaa007 · Cited by 203 · see citing works
Ryan, E., Hore, K., Power, J., & Jackson, T. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1133484 · Cited by 172 · see citing works
Carpenter, L. M., Swerdlow, A. J., & Fear, N. T. (1997) · Occupational and Environmental Medicine · doi:10.1136/oem.54.6.388 · Cited by 153 · see citing works
Implementing Optimal Team-Based Care to Reduce Clinician Burnout
Smith, C. D., Balatbat, C., Corbridge, S., Dopp, A., American Society of Health-System Pharmacists, Fried, J. R., Harter, R., American Society of Anesthesiologists, Landefeld, S., Martin, C. Y., et al. (2018) · NAM Perspectives · doi:10.31478/201809c · Cited by 130 · see citing works
AI's role in revolutionizing personalized medicine by reshaping pharmacogenomics and drug therapy
Taherdoost, H., & Ghofrani, A. (2024) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.08.005 · Cited by 125 · see citing works
Kernberg, A., Gold, J. A., & Mohan, V. (2024) · Journal of Medical Internet Research · doi:10.2196/54419 · Cited by 120 · see citing works
Documentation burden in nursing and its role in clinician burnout syndrome
Gesner, E., Dykes, P. C., Zhang, L., & Gazarian, P. (2022) · Applied Clinical Informatics · doi:10.1055/s-0042-1757157 · Cited by 118 · see citing works
Ye, J., Yao, L., Shen, J., Janarthanam, R., & Luo, Y. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01318-4 · Cited by 105 · see citing works
Albrecht, M., Shanks, D., Shah, T., Hudson, T., Thompson, J., Filardi, T., Wright, K., Ator, G. A., & Smith, T. R. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooaf013 · Cited by 103 · see citing works
Speech recognition for clinical documentation from 1990 to 2018: a systematic review
Blackley, S. V., Huynh, J., Wang, L., Korach, Z., & Zhou, L. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy179 · Cited by 97 · see citing works
Owens, L. M., Wilda, J. J., Hahn, P. Y., Koehler, T., & Fletcher, J. J. (2024) · Family Practice · doi:10.1093/fampra/cmad092 · Cited by 86 · see citing works
Building the evidence-base to reduce electronic health record–related clinician burden
Dymek, C., Kim, B. I., Melton, G. B., Payne, T. H., Singh, H., & Hsiao, C. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa238 · Cited by 83 · see citing works
Harnessing the Power of Generative AI for Clinical Summaries: Perspectives From Emergency Physicians
Barak-Corren, Y., Wolf, R., Rozenblum, R., Creedon, J. K., Lipsett, S. C., Lyons, T. W., Michelson, K. A., Miller, K. A., Shapiro, D. J., Reis, B. Y., et al. (2024) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2024.01.039 · Cited by 75 · see citing works
Electronic Health Records and Improved Patient Care: Opportunities for Applied Psychology
Ratwani, R. M. (2017) · Current Directions in Psychological Science · doi:10.1177/0963721417700691 · Cited by 70 · see citing works
Murphy, D. R., Satterly, T., Giardina, T. D., Sittig, D. F., & Singh, H. (2019) · Journal of General Internal Medicine · doi:10.1007/s11606-019-05112-5 · Cited by 55 · see citing works
A method to automate the discharge summary hospital course for neurology patients
Hartman, V., Bapat, S. S., Weiner, M. G., Navi, B. B., Sholle, E., & Campion, T. R. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad177 · Cited by 44 · see citing works
Learning from the EHR to implement AI in healthcare
Rose, C. C., & Chen, J. H. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01340-0 · Cited by 42 · see citing works
Sutton, D. E., Fogel, J. R., Giard, A., Gulker, L. A., Ivory, C. H., & Rosa, A. M. (2020) · Applied Clinical Informatics · doi:10.1055/s-0040-1713634 · Cited by 31 · see citing works
Yang, J. C., So, J., Zhang, H., Jones, S., Connolly, D. M., Golding, C., Griffes, E., Szerencsy, A. C., Wu, T., Aphinyanaphongs, Y., et al. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae078 · Cited by 26 · see citing works
Liu, H., Lou, S. S., Warner, B. C., Harford, D. R., Kannampallil, T., & Lu, C. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539056 · Cited by 24 · see citing works
Internal Medicine Residents' Computer Use in the Inpatient Setting
Oxentenko, A. S., Manohar, C. U., McCoy, C. P., Bighorse, W. K., McDonald, F. S., Kolars, J. C., & Levine, J. A. (2012) · Journal of Graduate Medical Education · doi:10.4300/jgme-d-12-00026.1 · Cited by 20 · see citing works
Creating Perinatal Nursing Care Plans Using ChatGPT
Johnson, L. G., Madandola, O. O., Santos, F. C. D., Priola, K., Yao, Y., Macieira, T. G. R., & Keenan, G. M. (2024) · The Journal of Perinatal & Neonatal Nursing · doi:10.1097/jpn.0000000000000831 · Cited by 19 · see citing works
Assessing the Efficacy and Clinical Utility of Artificial Intelligence Scribes in Urology
Moryousef, J., Nadesan, P., Uy, M., Matti, D., & Guo, Y. (2024) · Urology · doi:10.1016/j.urology.2024.11.061 · Cited by 18 · see citing works
Laing, S., & Mercer, J. G. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01675-8 · Cited by 17 · see citing works
Hamad, M. M. E., & Bah, S. M. (2022) · The Canadian Journal of Hospital Pharmacy · doi:10.4212/cjhp.3223 · Cited by 12 · see citing works
The influence of electronic health record use on healthcare providers burnout
Alumran, A. K., Aljuraifani, S. A., Almousa, Z. A., Hariri, B., Aldossary, H., Aljuwair, M. M., Al-Kahtani, N. K., & Alissa, K. A. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101588 · Cited by 7 · see citing works
Wassell, M., Vitiello, A., Butler-Henderson, K. A., Verspoor, K. M., McCann, P., & Pollard, H. P. (2024) · Journal of Occupational Rehabilitation · doi:10.1007/s10926-024-10175-1 · Cited by 6 · see citing works
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Etagiuri, A., Chaudhry, D., van der Merwe, J., Nickol, M., & van der Merwe, J. M. (2026) · The Journal of Arthroplasty · doi:10.1016/j.arth.2026.03.069 · Cited by 8 · see citing works
Sani, H. S., Mobaraki, M., Sholibor, A., Fallah, S., Abdi, F., Hasannezhad, M., & Jandaghian-Bidgoli, M. (2026) · BMC Family Practice · doi:10.1186/s12875-025-03157-6 · Cited by 7 · see citing works
Lipschuetz, M., & Topaz, M. (2026) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2026.105369 · Cited by 6 · see citing works
Elsayed, N. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106419 · Cited by 3 · see citing works
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Regmi, A., Jain, V., Plate, J. F., Tafti, A. P., Baral, S., & Jain, V. K. (2026) · Indian Journal of Orthopaedics · doi:10.1007/s43465-026-01874-3 · Cited by 2 · see citing works
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- Authors
- Sarraf, B., & Ghasempour, A.
- Affiliations
- Tallinn University of Technology (EE)
- Corresponding author
- Sarraf, Berna
- Ghasempour, Ali
- Venue
- Frontiers in Public Health
- Publisher
- Frontiers Media SA
- Published
- 2025-07-03
- Volume
- 13
- Pages
- 1628831-1628831
- ISSN
- 2296-2565 (ISSN-L)
- PMC
- PMC12267243
- OpenAlex
- W4412012638
- Cited by
- 40
- References
- 45
- Citations by year
- 2026: 36 · 2025: 4
- FWCI
- 5.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Healthcare professionals’ stress and burnout
- Keywords
- artificial intelligence, electronic health records, burnout, healthcare workers, clinical documentation burden, ambient AI scribes, clinical decision support systems, large language models, natural language processing, emotional exhaustion, depersonalization, personal accomplishment
- MeSH terms
- Artificial Intelligence, Burnout, Professional, Health Personnel, Humans, Electronic Health Records
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