This paper is in the following e-collection/theme issue:

Applications of AI (308) Artificial Intelligence (2229) Electronic Health Records (1345) Ambient AI Scribes and AI-Driven Documentation Technologies (18) Safety and Error Prevention in Health (247) Clinical Informatics (1373) Computerized History Taking (14)

Published on in Vol 3 (2024)

Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study

Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study

Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study

Authors of this article:

Marieke Meija van Buchem1 Author Orcid Image ;   Ilse M J Kant2 Author Orcid Image ;   Liza King3 Author Orcid Image ;   Jacqueline Kazmaier3 Author Orcid Image ;   Ewout W Steyerberg4 Author Orcid Image ;   Martijn P Bauer5 Author Orcid Image
Article Authors Cited by (31) Tweetations Metrics

Journals

  1. Alli S, Hossain S, Das S, Upshur R. The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education. JMIR Medical Education 2024;10:e51446 View
  2. Saadat S, Khalilizad Darounkolaei M, Qorbani M, Hemmat A, Hariri S. Enhancing Clinical Documentation with AI: Reducing Errors, Improving Interoperability, and Supporting Real-Time Note-Taking. InfoScience Trends 2025;2(1):1 View
  3. Biro J, Handley J, Cobb N, Kottamasu V, Collins J, Krevat S, Ratwani R. Accuracy and Safety of AI-Enabled Scribe Technology: Instrument Validation Study. Journal of Medical Internet Research 2025;27:e64993 View
  4. Rashid Z, Ahmed H, Nadeem N, Zafar S, Yousaf M. The paradigm of digital health: AI applications and transformative trends. Neural Computing and Applications 2025;37(17):11039 View
  5. Gilchrist C, Goldszmidt M. Proceed with caution: Conceptualizing the risk of artificial intelligence scribes in medical education. Medical Teacher 2026;48(1):1 View
  6. Ha E, Choon-Kon-Yune I, Murray L, Luan S, Montague E, Bhattacharyya O, Agarwal P. Evaluating the Usability, Technical Performance, and Accuracy of Artificial Intelligence Scribes for Primary Care: Competitive Analysis. JMIR Human Factors 2025;12:e71434 View
  7. Sasseville M, Yousefi F, Ouellet S, Naye F, Stefan T, Carnovale V, Bergeron F, Ling L, Gheorghiu B, Hagens S, Gareau-Lajoie S, LeBlanc A. The Impact of AI Scribes on Streamlining Clinical Documentation: A Systematic Review. Healthcare 2025;13(12):1447 View
  8. Ng J, Wang E, Zhou X, Zhou K, Goh C, Sim G, Tan H, Goh S, Ng Q. Evaluating the performance of artificial intelligence-based speech recognition for clinical documentation: a systematic review. BMC Medical Informatics and Decision Making 2025;25(1) View
  9. Devereux C, Jacob B, Araz K, Bullen C, Murphy C, Redmond P. Protocol for a Scoping Review of Digital Scribes in Primary Care: Design, Implementation, Accuracy and Impact. HRB Open Research 2025;8:79 View
  10. Clusmann J, Balaguer-Montero M, Bassegoda O, Schneider C, Seraphin T, Paintsil E, Luedde T, Lopez R, Calderaro J, Gilbert S, Marjot T, Spann A, Shawcross D, Lens S, Trépo E, Kather J. The barriers to uptake of artificial intelligence in hepatology and how to overcome them. Journal of Hepatology 2025;83(6):1410 View
  11. Pérez Martínez D. Proposal for Responsible Use of Generative Artificial Intelligence in Medical Practice. Revista de Neurología 2025;80(7) View
  12. Draper T, Leake J, Lamb-Riddell K, Cox T, McCormick J, Trowell S, Kiely J, Luxton R. Impact of acoustic and informational noise on AI-generated clinical summaries. BMJ Digital Health & AI 2025;1(1):e000057 View
  13. Draper T, Cox T, Lamb-Riddell K, Moretti L, McCormick J, Trowell S, Kiely J, Luxton R. Clinical AI Scribes in primary care: accuracy, error severity and implications for clinical practice. BMJ Digital Health & AI 2025;1(1):e000092 View
  14. Leiserowitz G, Mansfield J, Tamakuwala S, MacDonald S, Jost M. Patient Attitudes Towards Ambient Voice Technology: A Pre-Implementation Patient Survey in an Academic Medical Center (Preprint). JMIR Medical Informatics 2025 View
  15. Anderson T, Mohan V, Dorr D, Ratwani R, Biro J, Gold J. Evaluating the Quality and Safety of Ambient Digital Scribe Platforms Using Simulated Ambulatory Encounters. Mayo Clinic Proceedings: Digital Health 2025;3(4):100292 View
  16. Moura L, Mishuris R, Metlay J, Habib M, Ting D, Gallagher K, Brodney S, Rieu-Werden M, Haas J. Hybrid Ambient Clinical Documentation and Physician Performance: Work Outside of Work, Documentation Delay, and Financial Productivity. Journal of General Internal Medicine 2025 View
  17. Van Tiem J, Cramer E, Iverson C, Kennelty K, Andrys N, Lee J, Knake L, Misurac J, Blum J, Reisinger H. Listening to the note: clinician perspectives on ambient artificial intelligence scribes in medical documentation. Journal of the American Medical Informatics Association 2026;33(2):255 View
  18. Clusmann J, Schneider C, Foersch S, Truhn D, Kather J. Künstliche Intelligenz: Chancen und Grenzen. Forum 2026;41(1):31 View
  19. Cittadino J, Traulsen P, Schmahl T, Wewetzer L, Cummerow J, Flägel K, Strumann C, Goetz K, Steinhäuser J. Information Extraction of Doctoral Theses Using Two Different Large Language Models vs Health Services Researchers: Development and Usability Study. JMIR Formative Research 2025;9:e77707 View
  20. Dundas N, Law T, Brender T, Mills H, Espejo E, A. Heintz T, Wallace A, Cobert J. All That Shines Is Not Gold: Maintaining Scientific Rigor When Evaluating, Interpreting, and Reviewing Studies Using Large Language Models. Anesthesiology 2026;144(2):272 View
  21. Yost C, Jumreornvong O, Hasoon J, Ruan Q, Ang S, Palumbo P, Bianco G, Yong R, Zetter G, Christo P, Kaye A, Hilger H, Chung M, Duszynski B, Robinson C. Artificial Intelligence-Assisted Scribing for Chronic Pain Care: A Narrative Review. Current Pain and Headache Reports 2026;30(1) View
  22. Woo B, Cato K, Cho H, You S, Song J. The use of large language models in clinical documentation: A scoping review. International Journal of Nursing Studies 2026;176:105322 View
  23. Zhao J, Liu H, Chen Y, Song F. Application of artificial intelligence tools and clinical documentation burden: a systematic review and meta-analysis. BMC Medical Informatics and Decision Making 2025;26(1) View
  24. Nair R, Hashmi M, Kassim S, Singer A. The Current State of Digital Scribes in Primary Care: A Scoping Review. Journal of Medical Systems 2026;50(1) View
  25. Brunner J, Morrissey S, Stevens E, Payne C, Wiltz S, Cutrona S, Rinne S. Ambient Scribe Technology in Simulated Patient Encounters Across Specialties. JAMA Network Open 2026;9(1):e2552870 View
  26. Irons J, Duenser A, Pickett T, Haysom G, McGrath M. AI in medical practice: doctors’ perspective on the benefits, challenges and facilitators of artificial intelligence scribe use. Health and Technology 2026 View
  27. Callens S. Effective prompt design for large language models in clinical practice. Acta Clinica Belgica 2026:1 View
  28. Atiku S, Olakotan O, Owolanke K. Usability‐Related Barriers and Facilitators Influencing the Adoption and Use of AI Scribes in Healthcare: A Scoping Review. Journal of Evaluation in Clinical Practice 2026;32(1) View

Books/Policy Documents

  1. R. Wojda T, Vino Dominic Savio F, Hochheiser H, Amaravathi A, Bharat K, Fischer G. Artificial Intelligence in Medicine and Surgery - An Exploration of Current Trends, Potential Opportunities, and Evolving Threats, Volume 3 [Working Title]. View
  2. Pathak A, Fernandes S, Biju A, Nayak J. ICT for Intelligent Systems. View

Dissertations

  1. . Implementation of Artificial Intelligence for Scribing in an Outpatient Mental Health Clinic. View