The impact of using AI-powered voice-to-text technology for clinical documentation on quality of care in primary care and outpatient settings: a systematic review
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Background
AI-powered Voice-to-text Technology (AIVT) offers a promising solution to reduce clinicians’ documentation burden during consultations, allowing more focus on patient interaction. This systematic review assesses AIVT’s impact on care quality in primary care and outpatient settings, focusing on seven components: effectiveness, efficiency, safety, patient-centredness, timeliness, equity, and integration.
Methods
A systematic search of five databases (Medline, Embase, Global Health, CINHAL, Scopus) was conducted for studies published up to September 20, 2024. Studies were included if they assessed the use of AIVT for medical documentation in primary care or outpatient settings, compared to manual or non-AI documentation methods, and reported outcomes relevant to the seven quality components. A narrative synthesis was conducted; meta-analysis was unfeasible due to study heterogeneity.
Findings
Of 1924 papers, nine studies were included (n = 524 healthcare professionals, n = 616 patients, 1069 consultations). Most (n = 7) were from the USA, with others in Bangladesh and the Philippines. All studies assessing effectiveness, patient-centredness, and efficiency (n = 9, 6, and 5, respectively) reported improvements, including faster documentation, reduced administrative burden, and enhanced patient-provider interaction. Safety findings were inconclusive; three of six studies raised concerns. Four studies highlighted seamless AIVT integration with Electronic Health Records, improving service timeliness. Three studies identified equity issues, referring to limited diversity and controlled simulation settings.
Interpretation
AIVT tools enhance documentation efficiency and patient-centred care, but concerns over transcription errors and generalisability warrant further testing in large-scale, diverse real-world settings.
Funding
This study was supported by the National Institute for Health and Care Research (NIHR) North-West London Patient Safety Research Collaboration (NIHR NWL PSRC, Ref. NIHR204292), with infrastructure support from the NIHR Imperial Biomedical Research Centre.
AI-powered Voice-to-text Technology (AIVT) offers a promising solution to reduce clinicians’ documentation burden during consultations, allowing more focus on patient interaction. This systematic review assesses AIVT’s impact on care quality in primary care and outpatient settings, focusing on seven components: effectiveness, efficiency, safety, patient-centredness, timeliness, equity, and integration.
Methods
A systematic search of five databases (Medline, Embase, Global Health, CINHAL, Scopus) was conducted for studies published up to September 20, 2024. Studies were included if they assessed the use of AIVT for medical documentation in primary care or outpatient settings, compared to manual or non-AI documentation methods, and reported outcomes relevant to the seven quality components. A narrative synthesis was conducted; meta-analysis was unfeasible due to study heterogeneity.
Findings
Of 1924 papers, nine studies were included (n = 524 healthcare professionals, n = 616 patients, 1069 consultations). Most (n = 7) were from the USA, with others in Bangladesh and the Philippines. All studies assessing effectiveness, patient-centredness, and efficiency (n = 9, 6, and 5, respectively) reported improvements, including faster documentation, reduced administrative burden, and enhanced patient-provider interaction. Safety findings were inconclusive; three of six studies raised concerns. Four studies highlighted seamless AIVT integration with Electronic Health Records, improving service timeliness. Three studies identified equity issues, referring to limited diversity and controlled simulation settings.
Interpretation
AIVT tools enhance documentation efficiency and patient-centred care, but concerns over transcription errors and generalisability warrant further testing in large-scale, diverse real-world settings.
Funding
This study was supported by the National Institute for Health and Care Research (NIHR) North-West London Patient Safety Research Collaboration (NIHR NWL PSRC, Ref. NIHR204292), with infrastructure support from the NIHR Imperial Biomedical Research Centre.
Date Issued
2025-08-01
Date Acceptance
2025-07-08
Citation
EBioMedicine, 2025, 118
ISSN
2352-3964
Publisher
Elsevier
Journal / Book Title
EBioMedicine
Volume
118
Copyright Statement
© 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
105861
Date Publish Online
2025-07-21