Evidence map›Paper›PMID 42832701›Full record

ArticleJMIR research protocols2026

Evaluation of Ambient Voice Technology in the National Health Service in England: Protocol for a Phase 2 Study.

Stephen Morris, Jenny Shand, Theo Georghiou, Kevin Herbert, Rachel Lawrence, Raj Mehta, Pei Li Ng, Lucina Rolewicz, Holly Elphinstone, Holly Walton

Abstract read
In one paragraph

Article in JMIR research protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Stephen MorrisDepartment of Public Health and Primary Care, University of Cambridge, East Forvie Building, Cambridge, CB2 0SR, United Kingdom, 44 1223 330300.ORCID http://orcid.org/0000-0002-5828-3563
Jenny ShandDepartment of Clinical Educational and Health Psychology, University College London, London, United Kingdom.ORCID http://orcid.org/0000-0002-4899-795X
Theo GeorghiouResearch and Policy, Nuffield Trust, London, United Kingdom.ORCID http://orcid.org/0000-0001-9532-876X
Kevin HerbertDepartment of Public Health and Primary Care, University of Cambridge, East Forvie Building, Cambridge, CB2 0SR, United Kingdom, 44 1223 330300.ORCID http://orcid.org/0009-0008-4354-7811
Rachel LawrenceResearch Department of Behavioural Science and Health, University College London, London, United Kingdom.ORCID http://orcid.org/0000-0003-3743-4622
Raj MehtaPublic Contributor, London, United Kingdom.ORCID http://orcid.org/0000-0002-4003-530X
Pei Li NgResearch Department of Behavioural Science and Health, University College London, London, United Kingdom.ORCID http://orcid.org/0000-0001-8411-220X
Lucina RolewiczResearch and Policy, Nuffield Trust, London, United Kingdom.ORCID http://orcid.org/0000-0003-4308-2408
Holly ElphinstoneResearch Department of Behavioural Science and Health, University College London, London, United Kingdom.ORCID http://orcid.org/0009-0007-3850-2128
Holly WaltonResearch Department of Behavioural Science and Health, University College London, London, United Kingdom.ORCID http://orcid.org/0000-0002-8746-059X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: 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. Objective: This study aims to explore the real-world impact of AVT on productivity, costs, and staff experience across NHS adult services. Methods: 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. Results: Data collection is expected to begin in August 2026 and conclude by January 2027. Publication of results is anticipated in February 2027. Conclusions: 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.

Indexed as

Artificial IntelligenceSpeech Recognition SoftwareCost-Benefit AnalysisEnglandHumansState MedicineAI scribesambient voice technologyartificial intelligenceclinical documentationhealth economicsmixed methodsNational Health ServiceNHSqualitative researchquasi-experiment

Identifiers

PMID42832701
PMCPMC13637992

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.