Sarraf Berna, Ghasempour Ali
Department of Health Technology, School of Information Technology, Tallinn University of Technology, Tallinn, Estonia.
School of Information Technology, IT College, Tallinn University of Technology, Tallinn, Estonia.
Front Public Health. 2025 Jul 3;13:1628831. doi: 10.3389/fpubh.2025.1628831. eCollection 2025.
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.
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.
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.
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.
电子健康记录（EHRs）的实施彻底改变了现代临床实践，提高了医疗效率、可及性和护理质量。然而，与电子健康记录相关的工作量被认为是导致医疗专业人员职业倦怠的一个重要因素，职业倦怠是一种与情感耗竭、去个性化和个人成就感降低相关的综合征。随着现代卫生系统探索技术解决方案，人工智能（AI）因其在促进文档处理和减轻认知负担方面的潜力而受到关注。本系统评价旨在探讨和了解人工智能对医疗专业人员中与电子健康记录相关的职业倦怠的影响。
按照PRISMA 2020指南进行系统文献综述。从三个电子数据库（PubMed、Scopus和Web of Science）中检索2019年至2025年发表的相关研究。检索策略包括三个主要领域：人工智能、电子健康记录和医疗专业人员职业倦怠。符合条件的研究包括经过同行评审的原创研究文章，这些文章评估了基于人工智能的技术对医疗专业人员职业倦怠的影响。筛选和选择过程按照PRISMA框架进行。使用乔安娜·布里格斯研究所批判性评价工具对纳入研究进行方法学质量评估。
在最初识别的287条记录中，有8项研究符合纳入标准。大多数已识别的研究在美国和加拿大进行。已识别的干预措施分为四个领域：环境人工智能抄写员、临床决策支持系统、大语言模型和自然语言处理工具。大多数研究侧重于减轻文档或收件箱相关的负担，并报告了积极结果，包括减少文档时间、提高工作流程效率以及减轻医疗专业人员的职业倦怠症状。然而，观察到一些方法学上的局限性，包括缺乏对照组、样本量小和随访期短，这限制了研究结果的普遍性。
将人工智能集成到电子健康记录系统中可能有潜力减轻文档负担和收件箱管理负担。尽管初步结果很有希望，但需要进一步进行方法学上更严谨的研究，以评估长期结果、评估不同临床环境中的可用性，并确保人工智能技术在常规医疗实践中的安全有效实施。
