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Barriers and opportunities of scaling ambient AI scribes for clinical documentation across diverse healthcare settings
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  • Perspective
  • Open access
  • Published: 23 March 2026

Barriers and opportunities of scaling ambient AI scribes for clinical documentation across diverse healthcare settings

  • Joshua W. Ohde1 na1,
  • Arjun Thompson2 na1,
  • Zhenghong Liu2,
  • Lauren M. Rost1,
  • Joshua D. Overgaard3,
  • Jonathan Yue En Tan4,
  • Shauna M. Overgaard1,5,
  • Raymond Francis R. Sarmiento6,
  • Yuhe Ke7,8,9,
  • Jonathan Chong Kai Liew7,10,
  • Jasmine Chiat Ling Ong7,11 &
  • …
  • Nan Liu7,12,13,14,15 

npj Digital Medicine , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

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  • Health care
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  • Social sciences

Abstract

Ambient AI scribes are reshaping clinical documentation and clinician-patient interactions. These tools were initially tested in low-acuity ambulatory settings. However, their deployment in diverse care settings raises new challenges. This perspective examines the clinical, technical, and ethical implications of ambient AI scribes in diverse settings. With thoughtful integration, ambient AI scribes can evolve into valuable assistive tools to clinicians. Responsible use of these tools can improve interactions with patients, enhance safety, reduce clinician burden, and improve care continuity.

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Acknowledgements

We sincerely thank Stephanie C. Bernthal, M.S., for designing the figures in this manuscript. This work was supported by the Duke-NUS Signature Research Programme funded by the Ministry of Health, Singapore. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Ministry of Health. This work is supported in part by generous funds from Dalio Philanthropies. The content expressed in this publication are those of the authors and does not necessarily represent the official views of Dalio Philanthropies.

Author information

Author notes
  1. These authors contributed equally: Joshua W. Ohde, Arjun Thompson.

Authors and Affiliations

  1. Center for Digital Health, Mayo Clinic, Rochester, MN, USA

    Joshua W. Ohde, Lauren M. Rost & Shauna M. Overgaard

  2. Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore

    Arjun Thompson & Zhenghong Liu

  3. Department of Medicine, General Internal Medicine, Mayo Clinic, Rochester, MN, USA

    Joshua D. Overgaard

  4. Department of Future Health Systems, Singapore General Hospital, Singapore, Singapore

    Jonathan Yue En Tan

  5. AI Validation & Stewardship Research Program, Mayo Clinic Health System, Rochester, MN, USA

    Shauna M. Overgaard

  6. Metro Pacific Health Tech Corporation, Pasig City, Philippines

    Raymond Francis R. Sarmiento

  7. Duke-NUS AI + Medical Sciences Initiative, Duke-NUS Medical School, Singapore, Singapore

    Yuhe Ke, Jonathan Chong Kai Liew, Jasmine Chiat Ling Ong & Nan Liu

  8. Department of Anesthesiology, Singapore General Hospital, Singapore, Singapore

    Yuhe Ke

  9. Data Science and Artificial Intelligence Lab, Singapore General Hospital, Singapore, Singapore

    Yuhe Ke

  10. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Jonathan Chong Kai Liew

  11. Division of Pharmacy, Singapore General Hospital, Singapore, Singapore

    Jasmine Chiat Ling Ong

  12. Centre for Biomedical Data Science, Duke-NUS Medical School, Singapore, Singapore

    Nan Liu

  13. Pre-hospital & Emergency Research Centre, Health Services Research and Population Health, Duke-NUS Medical School, Singapore, Singapore

    Nan Liu

  14. NUS Artificial Intelligence Institute, National University of Singapore, Singapore, Singapore

    Nan Liu

  15. Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA

    Nan Liu

Authors
  1. Joshua W. Ohde
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Contributions

J.W.O. and A.T. contributed equally. Initial conceptualization: J.W.O., A.T., Z.L., J.Y.E.T., and J.C.L.O. Drafting of the first manuscript: J.W.O., A.T., Z.L., L.M.R., J.D.O., J.Y.E.T., S.M.O., and J.C.L.O. Critical revision of the manuscript: J.W.O., A.T., Z.L., L.M.R., J.D.O., J.Y.E.T., S.M.O., R.F.R.S., Y.K., J.C.K.L., J.C.L.O., and N.L. All authors have read, reviewed, and approved the final manuscript.

Corresponding authors

Correspondence to Jasmine Chiat Ling Ong or Nan Liu.

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Competing interests

Author J.C.L.O is an associate editor of npj digital medicine. N.L. is an editorial board member of npj digital medicine. J.C.L.O and N.L. were not involved in the journal’s review of, or decisions related to, this manuscript.

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Ohde, J.W., Thompson, A., Liu, Z. et al. Barriers and opportunities of scaling ambient AI scribes for clinical documentation across diverse healthcare settings. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02554-0

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  • Received: 21 October 2025

  • Accepted: 06 March 2026

  • Published: 23 March 2026

  • DOI: https://doi.org/10.1038/s41746-026-02554-0

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