Treffer: The Current State of Digital Scribes in Primary Care: A Scoping Review.
Title:
The Current State of Digital Scribes in Primary Care: A Scoping Review.
Authors:
Nair R; Health Information Science, University of Victoria, Victoria, BC, Canada. rajeshnair@uvic.ca., Hashmi MM; Department of Family Medicine, University of Manitoba, Winnipeg, MB, Canada., Kassim SS; Department of Family Medicine, University of Manitoba, Winnipeg, MB, Canada., Singer A; Department of Family Medicine, University of Manitoba, Winnipeg, MB, Canada.
Source:
Journal of medical systems [J Med Syst] 2026 Jan 03; Vol. 50 (1), pp. 2. Date of Electronic Publication: 2026 Jan 03.
Publication Type:
Journal Article; Scoping Review
Language:
English
Journal Info:
Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 7806056 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-689X (Electronic) Linking ISSN: 01485598 NLM ISO Abbreviation: J Med Syst Subsets: MEDLINE
Imprint Name(s):
Publication: 1999- : New York, NY : Kluwer Academic/Plenum Publishers
Original Publication: New York, Plenum Press.
Original Publication: New York, Plenum Press.
MeSH Terms:
References:
Coiera E, Kocaballi B, Halamka J, Laranjo L: The digital scribe. NPJ Digit Med 2018, 1:58. (PMID: 10.1038/s41746-018-0066-9)
Coiera E, Liu S: Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare. Cell Rep Med 2022, 3(12):100860. (PMID: 10.1016/j.xcrm.2022.100860)
van Buchem MM, Boosman H, Bauer MP, Kant IMJ, Cammel SA, Steyerberg EW: The digital scribe in clinical practice: a scoping review and research agenda. NPJ DIGITAL MEDICINE 2021, 4(1).
Agarwal P, Lall R, Girdhari R: Artificial intelligence scribes in primary care. CMAJ 2024, 196(30):E1042. (PMID: 10.1503/cmaj.240363)
Falcetta FS, de Almeida FK, Lemos JCS, Goldim JR, da Costa CA: Automatic documentation of professional health interactions: A systematic review. Artif Intell Med 2023, 137:102487. (PMID: 10.1016/j.artmed.2023.102487)
Jaddoh A, Loizides F, Rana O: Interaction between people with dysarthria and speech recognition systems: A review. Assist Technol 2023, 35(4):330–338. (PMID: 10.1080/10400435.2022.2061085)
Davenport T, Kalakota R: The potential for artificial intelligence in healthcare. Future Healthc J 2019, 6(2):94–98. (PMID: 10.7861/futurehosp.6-2-94)
Mukherjee J, Sharma R, Dutta P, Bhunia B: Artificial intelligence in healthcare: a mastery. Biotechnol Genet Eng Rev 2024, 40(3):1659–1708. (PMID: 10.1080/02648725.2023.2196476)
Liu J, Yang M, Yu Y, Xu H, Li K, Zhou X: Large language models in bioinformatics: applications and perspectives. ArXiv 2024.
Campino J: Unleashing the transformers: NLP models detect AI writing in education. J Comput Educ 2024.
Yang X, Chen A, PourNejatian Nea: A large language model for electronic health records. NPJ Digit Med 2022, 5(194).
van Buchem MM, Boosman H, Bauer MP, Kant IMJ, Cammel SA, Steyerberg EW: The digital scribe in clinical practice: a scoping review and research agenda. NPJ Digit Med 2021, 4(1):57. (PMID: 10.1038/s41746-021-00432-5)
Tran BD, Chen Y, Liu S, Zheng K: How does medical scribes’ work inform development of speech-based clinical documentation technologies? A systematic review. J Am Med Inform Assoc 2020, 27(5):808–817. (PMID: 10.1093/jamia/ocaa020)
McCoy LG, Ci Ng FY, Sauer CM, Yap Legaspi KE, Jain B, Gallifant J, McClurkin M, Hammond A, Goode D, Gichoya J et al: Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: a narrative review. BMC Med Educ 2024, 24(1):1096. (PMID: 10.1186/s12909-024-06048-z)
van Buchem MM, Kant IMJ, King L, Kazmaier J, Steyerberg EW, Bauer MP: Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study. JMIR AI 2024, 3:e60020. (PMID: 10.2196/60020)
Quiroz JC, Laranjo L, Kocaballi AB, Berkovsky S, Rezazadegan D, Coiera E: Challenges of developing a digital scribe to reduce clinical documentation burden. NPJ Digit Med 2019, 2:114. (PMID: 10.1038/s41746-019-0190-1)
Iyamu I, McKee G, Haag D, Gilbert M: Defining the role of digital public health in the evolving digital health landscape: policy and practice implications in Canada. Health Promot Chronic Dis Prev Can 2024, 44(2):66–69. (PMID: 10.24095/hpcdp.44.2.04)
Cao DY, Silkey JR, Decker MC, Wanat KA: Artificial intelligence-driven digital scribes in clinical documentation: Pilot study assessing the impact on dermatologist workflow and patient encounters. JAAD Int 2024, 15:149–151. (PMID: 10.1016/j.jdin.2024.02.009)
Cao Y, Kunaprayoon D, Ren L: Interpretable AI-assisted clinical decision making (CDM) for dose prescription in radiosurgery of brain metastases. Radiother Oncol 2023, 187:109842. (PMID: 10.1016/j.radonc.2023.109842)
Wang J, Lavender M, Hoque E, Brophy P, Kautz H: A patient-centered digital scribe for automatic medical documentation. JAMIA Open 2021, 4(1):ooab003. (PMID: 10.1093/jamiaopen/ooab003)
Zhang Y, Wang X, Hou Z, Li J: Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods. JMIR Med Inform 2018, 6(4):e50. (PMID: 10.2196/medinform.9965)
Ghatnekar S, Faletsky A, Nambudiri VE: Digital scribe utility and barriers to implementation in clinical practice: a scoping review. Health Technol (Berl) 2021, 11(4):803–809. (PMID: 10.1007/s12553-021-00568-0)
Sloss EA, Abdul S, Aboagyewah MA, Beebe A, Kendle K, Marshall K, Rosenbloom ST, Rossetti S, Grigg A, Smith KD et al: Toward Alleviating Clinician Documentation Burden: A Scoping Review of Burden Reduction Efforts. Appl Clin Inform 2024, 15(3):446–455. (PMID: 10.1055/s-0044-1787007)
Chapter 11: Scoping Reviews. JBI Manual for Evidence Synthesis [Internet] [ https://doi.org/10.46658/JBIMES - 20 – 12.].
Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, Moher D, Peters MDJ, Horsley T, Weeks L et al: PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 2018, 169(7):467–473. (PMID: 10.7326/M18-0850)
Coiera E: The Price of Artificial Intelligence. Yearb Med Inform 2019, 28(1):14–15. (PMID: 10.1055/s-0039-1677892)
Seth P, Carretas R, Rudzicz F: The Utility and Implications of Ambient Scribes in Primary Care. Jmir ai 2024, 3:e57673. (PMID: 10.2196/57673)
Ghatnekar S, Faletsky A, Nambudiri VE: Digital scribes in dermatology: Implications for practice. J Am Acad Dermatol 2022, 86(4):968–969. (PMID: 10.1016/j.jaad.2021.11.011)
Han B, Zhu H, Zhou S, Ahmed S, Mush M: HuskyScribe at MEDIQA-Sum2023: Summarizing clinical dialogues. In: MEDIQA-Sum: 2023; Thessaloniki, Greece; 2023.
Islam MN, Mim TM, Tasfia T, Hossain MM: Enhancing patient treatment though automation: The development of an efficient scribe and prescribe system. J IMU 2024, 45.
Li B, Crampton N, Yeates T, Xia Y, Tian X, Truong K: Automating clinical documentation with digital scribes: Understanding the impact on physicians. In: CHI ‘21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems: 2021; Yokohama, Japan: Association for Computing Machinery; 2021.
Magyari R, Secomandi F: Service blueprinting for better collaboration in human-centric AI: The design of a digital scribe for orthopedic consultation. Int J Design 2023, 17(3):63–77.
Sezgin E, Sirrianni J, Kranz K: Development and Evaluation of a Digital Scribe: Conversation Summarization Pipeline for Emergency Department Counseling Sessions towards Reducing Documentation Burden. medRxiv 2023.
Sezgin E, Sirrianni JW, Kranz K: Evaluation of a Digital Scribe: Conversation Summarization for Emergency Department Consultation Calls. Appl Clin Inform 2024, 15(3):600–611. (PMID: 10.1055/a-2327-4121)
Avendano JP, Gallagher DO, Hawes JD, Boyle J, Glasser L, Aryee J, Katt BM: Interfacing With the Electronic Health Record (EHR): A Comparative Review of Modes of Documentation. Cureus 2022, 14(6):e26330.
Pavuluri S, Sangal R, Sather J, Taylor RA: Balancing act: the complex role of artificial intelligence in addressing burnout and healthcare workforce dynamics. BMJ Health Care Inform 2024, 31(1).
Kocaballi AB, Coiera E, Tong HL, White SJ, Quiroz JC, Rezazadegan F, Willcock S, Laranjo L: A network model of activities in primary care consultations. J Am Med Inform Assoc 2019, 26(10):1074–1082. (PMID: 10.1093/jamia/ocz046)
Owens LM, Wilda JJ, Hahn PY, Koehler T, Fletcher JJ: The association between use of ambient voice technology documentation during primary care patient encounters, documentation burden, and provider burnout. Fam Pract 2024, 41(2):86–91. (PMID: 10.1093/fampra/cmad092)
Hudelson C, Gunderson MA, Pestka D, Christiaansen T, Stotka B, Kissock L, Markowitz R, Badlani S, Melton GB: Selection and Implementation of Virtual Scribe Solutions to Reduce Documentation Burden: A Mixed Methods Pilot. AMIA Jt Summits Transl Sci Proc 2024, 2024:230–238.
Nguyen OT, Turner K, Charles D, Sprow O, Perkins R, Hong YR, Islam JY, Khanna N, Alishahi Tabriz A, Hallanger-Johnson J et al: Implementing Digital Scribes to Reduce Electronic Health Record Documentation Burden Among Cancer Care Clinicians: A Mixed-Methods Pilot Study. JCO Clin Cancer Inform 2023, 7:e2200166. (PMID: 10.1200/CCI.22.00166)
Tran BD, Latif K, Reynolds TL, Park J, Elston Lafata J, Tai-Seale M, Zheng K: “Mm-hm,” “Uh-uh”: are non-lexical conversational sounds deal breakers for the ambient clinical documentation technology? J Am Med Inform Assoc 2023, 30(4):703–711. (PMID: 10.1093/jamia/ocad001)
Tran BD, Mangu R, Tai-Seale M, Lafata JE, Zheng K: Automatic speech recognition performance for digital scribes: a performance comparison between general-purpose and specialized models tuned for patient-clinician conversations. AMIA Annu Symp Proc 2022, 2022:1072–1080.
Haberle T, Cleveland C, Snow GL, Barber C, Stookey N, Thornock C, Younger L, Mullahkhel B, Ize-Ludlow D: The impact of nuance DAX ambient listening AI documentation: a cohort study. J Am Med Inform Assoc 2024, 31(4):975–979. (PMID: 10.1093/jamia/ocae022)
Quiroz JC, Laranjo L, Kocaballi AB, Briatore A, Berkovsky S, Rezazadegan D, Coiera E: Identifying relevant information in medical conversations to summarize a clinician-patient encounter. Health Informatics J 2020, 26(4):2906–2914. (PMID: 10.1177/1460458220951719)
Yeung T: What’s the difference between edge computing and cloud computing? In: NVIDIA. vol. 2025; 2022.
Siniosoglou I, Bibi S, Kollias KF, Fragulis G, Radoglou-Grammatikis P, Lagkas T, Argyriou V, Vitsas V, Sarigiannidis P: Federated Learning Models in Decentralized Critical Infrastructure. In: Shaping the Future of IoT with Edge Intelligence: How Edge Computing Enables the Next Generation of IoT Applications. edn. Edited by Sofia RC, Soldatos J. Abingdon (UK); 2024: 95–115.
MacFarlanes: Human-in-the-loop: The role of oversight in the world of generative AI. In., vol. 2025; 2025.
Coiera E, Liu S: Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare. Cell Rep Med 2022, 3(12):100860. (PMID: 10.1016/j.xcrm.2022.100860)
van Buchem MM, Boosman H, Bauer MP, Kant IMJ, Cammel SA, Steyerberg EW: The digital scribe in clinical practice: a scoping review and research agenda. NPJ DIGITAL MEDICINE 2021, 4(1).
Agarwal P, Lall R, Girdhari R: Artificial intelligence scribes in primary care. CMAJ 2024, 196(30):E1042. (PMID: 10.1503/cmaj.240363)
Falcetta FS, de Almeida FK, Lemos JCS, Goldim JR, da Costa CA: Automatic documentation of professional health interactions: A systematic review. Artif Intell Med 2023, 137:102487. (PMID: 10.1016/j.artmed.2023.102487)
Jaddoh A, Loizides F, Rana O: Interaction between people with dysarthria and speech recognition systems: A review. Assist Technol 2023, 35(4):330–338. (PMID: 10.1080/10400435.2022.2061085)
Davenport T, Kalakota R: The potential for artificial intelligence in healthcare. Future Healthc J 2019, 6(2):94–98. (PMID: 10.7861/futurehosp.6-2-94)
Mukherjee J, Sharma R, Dutta P, Bhunia B: Artificial intelligence in healthcare: a mastery. Biotechnol Genet Eng Rev 2024, 40(3):1659–1708. (PMID: 10.1080/02648725.2023.2196476)
Liu J, Yang M, Yu Y, Xu H, Li K, Zhou X: Large language models in bioinformatics: applications and perspectives. ArXiv 2024.
Campino J: Unleashing the transformers: NLP models detect AI writing in education. J Comput Educ 2024.
Yang X, Chen A, PourNejatian Nea: A large language model for electronic health records. NPJ Digit Med 2022, 5(194).
van Buchem MM, Boosman H, Bauer MP, Kant IMJ, Cammel SA, Steyerberg EW: The digital scribe in clinical practice: a scoping review and research agenda. NPJ Digit Med 2021, 4(1):57. (PMID: 10.1038/s41746-021-00432-5)
Tran BD, Chen Y, Liu S, Zheng K: How does medical scribes’ work inform development of speech-based clinical documentation technologies? A systematic review. J Am Med Inform Assoc 2020, 27(5):808–817. (PMID: 10.1093/jamia/ocaa020)
McCoy LG, Ci Ng FY, Sauer CM, Yap Legaspi KE, Jain B, Gallifant J, McClurkin M, Hammond A, Goode D, Gichoya J et al: Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: a narrative review. BMC Med Educ 2024, 24(1):1096. (PMID: 10.1186/s12909-024-06048-z)
van Buchem MM, Kant IMJ, King L, Kazmaier J, Steyerberg EW, Bauer MP: Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study. JMIR AI 2024, 3:e60020. (PMID: 10.2196/60020)
Quiroz JC, Laranjo L, Kocaballi AB, Berkovsky S, Rezazadegan D, Coiera E: Challenges of developing a digital scribe to reduce clinical documentation burden. NPJ Digit Med 2019, 2:114. (PMID: 10.1038/s41746-019-0190-1)
Iyamu I, McKee G, Haag D, Gilbert M: Defining the role of digital public health in the evolving digital health landscape: policy and practice implications in Canada. Health Promot Chronic Dis Prev Can 2024, 44(2):66–69. (PMID: 10.24095/hpcdp.44.2.04)
Cao DY, Silkey JR, Decker MC, Wanat KA: Artificial intelligence-driven digital scribes in clinical documentation: Pilot study assessing the impact on dermatologist workflow and patient encounters. JAAD Int 2024, 15:149–151. (PMID: 10.1016/j.jdin.2024.02.009)
Cao Y, Kunaprayoon D, Ren L: Interpretable AI-assisted clinical decision making (CDM) for dose prescription in radiosurgery of brain metastases. Radiother Oncol 2023, 187:109842. (PMID: 10.1016/j.radonc.2023.109842)
Wang J, Lavender M, Hoque E, Brophy P, Kautz H: A patient-centered digital scribe for automatic medical documentation. JAMIA Open 2021, 4(1):ooab003. (PMID: 10.1093/jamiaopen/ooab003)
Zhang Y, Wang X, Hou Z, Li J: Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods. JMIR Med Inform 2018, 6(4):e50. (PMID: 10.2196/medinform.9965)
Ghatnekar S, Faletsky A, Nambudiri VE: Digital scribe utility and barriers to implementation in clinical practice: a scoping review. Health Technol (Berl) 2021, 11(4):803–809. (PMID: 10.1007/s12553-021-00568-0)
Sloss EA, Abdul S, Aboagyewah MA, Beebe A, Kendle K, Marshall K, Rosenbloom ST, Rossetti S, Grigg A, Smith KD et al: Toward Alleviating Clinician Documentation Burden: A Scoping Review of Burden Reduction Efforts. Appl Clin Inform 2024, 15(3):446–455. (PMID: 10.1055/s-0044-1787007)
Chapter 11: Scoping Reviews. JBI Manual for Evidence Synthesis [Internet] [ https://doi.org/10.46658/JBIMES - 20 – 12.].
Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, Moher D, Peters MDJ, Horsley T, Weeks L et al: PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 2018, 169(7):467–473. (PMID: 10.7326/M18-0850)
Coiera E: The Price of Artificial Intelligence. Yearb Med Inform 2019, 28(1):14–15. (PMID: 10.1055/s-0039-1677892)
Seth P, Carretas R, Rudzicz F: The Utility and Implications of Ambient Scribes in Primary Care. Jmir ai 2024, 3:e57673. (PMID: 10.2196/57673)
Ghatnekar S, Faletsky A, Nambudiri VE: Digital scribes in dermatology: Implications for practice. J Am Acad Dermatol 2022, 86(4):968–969. (PMID: 10.1016/j.jaad.2021.11.011)
Han B, Zhu H, Zhou S, Ahmed S, Mush M: HuskyScribe at MEDIQA-Sum2023: Summarizing clinical dialogues. In: MEDIQA-Sum: 2023; Thessaloniki, Greece; 2023.
Islam MN, Mim TM, Tasfia T, Hossain MM: Enhancing patient treatment though automation: The development of an efficient scribe and prescribe system. J IMU 2024, 45.
Li B, Crampton N, Yeates T, Xia Y, Tian X, Truong K: Automating clinical documentation with digital scribes: Understanding the impact on physicians. In: CHI ‘21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems: 2021; Yokohama, Japan: Association for Computing Machinery; 2021.
Magyari R, Secomandi F: Service blueprinting for better collaboration in human-centric AI: The design of a digital scribe for orthopedic consultation. Int J Design 2023, 17(3):63–77.
Sezgin E, Sirrianni J, Kranz K: Development and Evaluation of a Digital Scribe: Conversation Summarization Pipeline for Emergency Department Counseling Sessions towards Reducing Documentation Burden. medRxiv 2023.
Sezgin E, Sirrianni JW, Kranz K: Evaluation of a Digital Scribe: Conversation Summarization for Emergency Department Consultation Calls. Appl Clin Inform 2024, 15(3):600–611. (PMID: 10.1055/a-2327-4121)
Avendano JP, Gallagher DO, Hawes JD, Boyle J, Glasser L, Aryee J, Katt BM: Interfacing With the Electronic Health Record (EHR): A Comparative Review of Modes of Documentation. Cureus 2022, 14(6):e26330.
Pavuluri S, Sangal R, Sather J, Taylor RA: Balancing act: the complex role of artificial intelligence in addressing burnout and healthcare workforce dynamics. BMJ Health Care Inform 2024, 31(1).
Kocaballi AB, Coiera E, Tong HL, White SJ, Quiroz JC, Rezazadegan F, Willcock S, Laranjo L: A network model of activities in primary care consultations. J Am Med Inform Assoc 2019, 26(10):1074–1082. (PMID: 10.1093/jamia/ocz046)
Owens LM, Wilda JJ, Hahn PY, Koehler T, Fletcher JJ: The association between use of ambient voice technology documentation during primary care patient encounters, documentation burden, and provider burnout. Fam Pract 2024, 41(2):86–91. (PMID: 10.1093/fampra/cmad092)
Hudelson C, Gunderson MA, Pestka D, Christiaansen T, Stotka B, Kissock L, Markowitz R, Badlani S, Melton GB: Selection and Implementation of Virtual Scribe Solutions to Reduce Documentation Burden: A Mixed Methods Pilot. AMIA Jt Summits Transl Sci Proc 2024, 2024:230–238.
Nguyen OT, Turner K, Charles D, Sprow O, Perkins R, Hong YR, Islam JY, Khanna N, Alishahi Tabriz A, Hallanger-Johnson J et al: Implementing Digital Scribes to Reduce Electronic Health Record Documentation Burden Among Cancer Care Clinicians: A Mixed-Methods Pilot Study. JCO Clin Cancer Inform 2023, 7:e2200166. (PMID: 10.1200/CCI.22.00166)
Tran BD, Latif K, Reynolds TL, Park J, Elston Lafata J, Tai-Seale M, Zheng K: “Mm-hm,” “Uh-uh”: are non-lexical conversational sounds deal breakers for the ambient clinical documentation technology? J Am Med Inform Assoc 2023, 30(4):703–711. (PMID: 10.1093/jamia/ocad001)
Tran BD, Mangu R, Tai-Seale M, Lafata JE, Zheng K: Automatic speech recognition performance for digital scribes: a performance comparison between general-purpose and specialized models tuned for patient-clinician conversations. AMIA Annu Symp Proc 2022, 2022:1072–1080.
Haberle T, Cleveland C, Snow GL, Barber C, Stookey N, Thornock C, Younger L, Mullahkhel B, Ize-Ludlow D: The impact of nuance DAX ambient listening AI documentation: a cohort study. J Am Med Inform Assoc 2024, 31(4):975–979. (PMID: 10.1093/jamia/ocae022)
Quiroz JC, Laranjo L, Kocaballi AB, Briatore A, Berkovsky S, Rezazadegan D, Coiera E: Identifying relevant information in medical conversations to summarize a clinician-patient encounter. Health Informatics J 2020, 26(4):2906–2914. (PMID: 10.1177/1460458220951719)
Yeung T: What’s the difference between edge computing and cloud computing? In: NVIDIA. vol. 2025; 2022.
Siniosoglou I, Bibi S, Kollias KF, Fragulis G, Radoglou-Grammatikis P, Lagkas T, Argyriou V, Vitsas V, Sarigiannidis P: Federated Learning Models in Decentralized Critical Infrastructure. In: Shaping the Future of IoT with Edge Intelligence: How Edge Computing Enables the Next Generation of IoT Applications. edn. Edited by Sofia RC, Soldatos J. Abingdon (UK); 2024: 95–115.
MacFarlanes: Human-in-the-loop: The role of oversight in the world of generative AI. In., vol. 2025; 2025.
Contributed Indexing:
Keywords: artificial intelligence scribes; automatic speech recognition; digital scribes; electronic scribes; natural language processing; primary care; scoping review
Entry Date(s):
Date Created: 20260103 Date Completed: 20260103 Latest Revision: 20260112
Update Code:
20260113
DOI:
10.1007/s10916-025-02319-4
PMID:
41483142
Database:
MEDLINE
Weitere Informationen
The purpose of this scoping review is to explore the current state of digital scribe technology in primary care, focusing on how automatic speech recognition (ASR) and natural language processing (NLP), which are foundational technologies behind artificial intelligence (AI) systems used in digital scribes contribute to their effectiveness, integration, and adoption. The Joanna Briggs Institute (JBI) guidelines for scoping reviews was utilized alongside reporting according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Searches through PubMed, Web of Science, Scopus, and Cumulative Index to Nursing and Allied Health Literature yielded 29 relevant studies from 14,866 studies, spanning six countries and from 2018 to 2024. Digital scribes demonstrated effectiveness in reducing documentation time, which directly enhances workflow efficiency and allows clinicians to spend more time interacting with patients. Digital scribes, while promising in improving clinical documentation, face significant integration challenges and adoption barriers, particularly in adapting to diverse healthcare workflows. The findings of this scoping review reveal several implications for the existing literature on digital scribes, particularly regarding the need for comprehensive studies on effectiveness in real-world primary care settings. This study highlights the promising role of digital scribes in primary care, where ASR and NLP technologies have demonstrated the potential to enhance documentation accuracy, streamline workflows, and reduce clinician burden.
(© 2026. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Declarations. Ethics Approval and Consent to Participate: Not applicable. This study did not involve human participants, patient data, or identifiable personal information. Consent for Publication: Not applicable. Competing interests: The authors declare no competing interests.