Evidence map›Paper›PMID 42732411›Full record

ReviewJournal of healthcare leadership2026

Implementation of Ambient AI Scribes in Hospitals: Lessons for Healthcare Leadership, Governance, and Change Management.

Kevin Xiang Zhou, Qin Xiang Ng, Hiang Khoon Tan, Edison Yi Wei Er, David T Wu, Thomas T Nguyen, Jordan Gigliotti, Walter L Siqueira, Nicholas Makhoul

Abstract readReview
In one paragraph

Review in Journal of healthcare leadership, 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

9 authors.

Kevin Xiang Zhou *Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.ORCID 0000-0003-0332-9947
Qin Xiang Ng *Research and Translational Innovation Office, SingHealth Community Hospitals, Singapore, Singapore.ORCID 0000-0001-8561-2513
Hiang Khoon TanSingHealth Duke-NUS Global Health Institute, Duke-NUS Medical School, Singapore, Singapore.ORCID 0000-0002-8565-2926
Edison Yi Wei ErLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.ORCID 0009-0001-5118-9056
David T WuDepartment of Oral Medicine, Infection and Immunity, Division of Periodontology, Harvard School of Dental Medicine, Boston, MA, USA.
Thomas T NguyenFaculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada.ORCID 0000-0002-1037-3068
Jordan GigliottiFaculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada.
Walter L SiqueiraCollege of Dentistry, University of Saskatchewan, Saskatoon, SK, Canada.ORCID 0000-0003-1921-3658
Nicholas MakhoulFaculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ambient artificial intelligence (AI) scribes are increasingly being adopted to address the clinical documentation burden associated with electronic health records (EHRs), which contributes to physician burnout, after-hours work, and reduced patient-clinician interaction. These systems capture clinician-patient conversations and use speech recognition and natural language processing (NLP) to generate draft clinical notes. Newer platforms are evolving into AI clinical copilots with additional functions such as pre-charting, clinical prompting, and safety checks. This article presents a structured, evidence-informed narrative synthesis and leadership analysis of ambient AI scribe implementation in hospital settings. Using Leavitt's Diamond model of organizational change, we examine the interdependent roles of Structure, Technology, People, and Process/Task in shaping successful adoption. Early evidence suggests meaningful reductions in documentation time, increased same-day note completion, and improvements in clinician-reported workload and engagement. However, certain risks and uncertainties remain, including transcription errors, hallucinated or omitted clinical content, variable performance across specialties, accents, and care settings, unresolved medico-legal liability, privacy/data-use concerns, uneven clinician adoption, and unclear short-term financial return on investment (ROI). We argue that the successful implementation of ambient AI scribes depends less on technical capability alone and more on prudent healthcare leadership and multidisciplinary governance. It also requires robust consent and data-protection frameworks, workflow redesign, clinician training, and ongoing monitoring of quality, safety, and well-being outcomes. Our principal contribution is a structured, domain-by-domain implementation framework that translates these requirements into concrete leadership questions, recommendations, accountable owners, and metrics for hospital leaders adopting ambient AI scribes. While ambient AI scribes promise to reduce administrative burden and restore time for patient-centered care, their potential will only be realized through strategically governed adoption aligned with an organization's culture and broader health-system priorities.

Indexed as

algorithmic governanceambient scribeclinical documentation automationdigital health equityelectronic health recordsimplementation sciencepatient safetyphysician burnout

Identifiers

PMID42732411
PMCPMC13570626

What OpenQuestion holds

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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.