Evaluation of Ambient Voice Technology in the National Health Service in England: Protocol for a Phase 2 Study.
Ambient voice technology (AVT) uses conversational AI to record and organize clinical consultations in real time. It is being adopted quickly across the National Health Service (NHS). However, evidence about its effects on productivity, costs, or staff experience across different health care settings is limited. Phase 1 of this research program developed a taxonomy, logic model, and outcome framework for evaluating AVT. Phase 2 will carry out a multisite, mixed methods evaluation of AVT in 4 NHS trusts. These include mental health outpatient services, acute hospital outpatient clinics, and accident and emergency departments.
This study aims to explore the real-world impact of AVT on productivity, costs, and staff experience across NHS adult services.
The study includes three parts: (1) a quantitative quasi-experimental analysis of routine NHS data to estimate the impact of AVT on documentation time, clinician activity, and service outcomes; (2) a comprehensive health economic evaluation comprising cost-consequence analysis, cost-benefit analysis, and budget impact modeling; and (3) interviews with up to 36 staff from 3 services (mental health outpatient, acute hospital outpatient, and accident and emergency) to explore their experience of using AVT, their views on its impact, and what helps and gets in the way of its use. Sites will be chosen to include different care settings, organizational environments, and AVT products. Quantitative and economic analyses will use NHS electronic health records and national datasets to understand changes over time and measure the impact of AVT. Interview data will be analyzed using thematic analysis and rapid assessment procedures to identify key themes. All study findings will be combined to give a clear view of AVT and its impact.
Data collection is expected to begin in August 2026 and conclude by January 2027. Publication of results is anticipated in February 2027.
This evaluation will provide real-world evidence on the productivity, economic, and experiential impacts of AVT in the NHS. Outputs will include peer-reviewed papers, a slide-deck summary for the funder, and a publicly available health economic decision-support tool to help NHS organizations decide whether they should adopt AVT.
This study aims to explore the real-world impact of AVT on productivity, costs, and staff experience across NHS adult services.
The study includes three parts: (1) a quantitative quasi-experimental analysis of routine NHS data to estimate the impact of AVT on documentation time, clinician activity, and service outcomes; (2) a comprehensive health economic evaluation comprising cost-consequence analysis, cost-benefit analysis, and budget impact modeling; and (3) interviews with up to 36 staff from 3 services (mental health outpatient, acute hospital outpatient, and accident and emergency) to explore their experience of using AVT, their views on its impact, and what helps and gets in the way of its use. Sites will be chosen to include different care settings, organizational environments, and AVT products. Quantitative and economic analyses will use NHS electronic health records and national datasets to understand changes over time and measure the impact of AVT. Interview data will be analyzed using thematic analysis and rapid assessment procedures to identify key themes. All study findings will be combined to give a clear view of AVT and its impact.
Data collection is expected to begin in August 2026 and conclude by January 2027. Publication of results is anticipated in February 2027.
This evaluation will provide real-world evidence on the productivity, economic, and experiential impacts of AVT in the NHS. Outputs will include peer-reviewed papers, a slide-deck summary for the funder, and a publicly available health economic decision-support tool to help NHS organizations decide whether they should adopt AVT.
Authors
Morris Morris, Shand Shand, Georghiou Georghiou, Herbert Herbert, Lawrence Lawrence, Mehta Mehta, Ng Ng, Rolewicz Rolewicz, Elphinstone Elphinstone, Walton Walton
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