Application of artificial intelligence tools and clinical documentation burden: a systematic review and meta-analysis

Zhao, Jungang, Liu, Hanxiang, Chen, Yaolong and Song, Fujian (2025) Application of artificial intelligence tools and clinical documentation burden: a systematic review and meta-analysis. BMC Medical Informatics and Decision Making. ISSN 1472-6947

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Abstract

Background Clinician burnout is a growing global concern, with heavy clinical documentation workload identified as a major contributor. Clinical documentation tasks, though essential for patient care and communication, are time-consuming and cognitively demanding. Recent advances in artificial intelligence (AI), particularly natural language processing and large language models, are being explored as potential tools to alleviate documentation burden, yet their quantitative impact has not been systematically assessed. Methods We performed a systematic review and meta-analysis, registered on PROSPERO (CRD420250653291) and guided by PRISMA. Eligible studies included frontline health professionals using AI tools for clinical note creation, with comparators being usual practice or pre-implementation baseline. Primary outcomes were documentation burden, workload, burnout, and time spent on documentation. Searches were conducted in PubMed, Web of Science, Scopus, and key journals. Effect sizes were synthesized using standardized mean difference (SMD) under a random-effects model, with subgroup analyses by study design, AI tool type, task type, editing status, and data origin. Results Of the 23 studies included, 12 were non-randomised studies with a concurrent control and 11 employed a before-and-after comparison design. The study participants varied in specialties and were mainly from ambulatory settings, including physicians, surgeons, pediatricians, and ICU specialists. Heterogeneity in results across included studies was considerable, and the methodological quality of the available studies was generally low. Pooling results of the 14 studies yielded an overall standardized mean difference (SMD) of -0.71 (95% confidence interval [CI]: -0.93 to -0.49), indicating a moderate reduction in documentation workload and related burnout. Based on results of studies in which clinicians reviewed and edited AI-generated drafts, AI applications reduced documentation time, similarly representing a moderate effect size (SMD= -0.72, 95% CI -0.99 to -0.45). The quality of notes generated by AI tools was at least comparable to those prepared manually by clinicians. Conclusions AI technologies offer promising benefits for reducing clinical documentation burden. However, their implementation must be accompanied by rigorous quality control and ongoing evaluation in practical settings to optimize their effectiveness and safeguard patient care outcomes.

Item Type:	Article
Additional Information:	Data availability All data generated or analysed during this study are included in this article and its supplementary information files.
Uncontrolled Keywords:	artificial intelligence,clinical documentation,large language models,burnout,workload,healthcare workers
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Centres > Public Health
Depositing User:	LivePure Connector
Date Deposited:	05 Jan 2026 11:30
Last Modified:	05 Jan 2026 12:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101512
DOI:	10.1186/s12911-025-03324-w

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