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Cited by 132Electronic Health Records SystemsArtificial Intelligence in Healthcare and EducationNursing Diagnosis and Documentation

The digital scribe

Open access
Coiera, Enrico, Kocaballi, Baki, Halamka, John, Laranjo, Liliana·2018·npj Digital Medicine
DOI 10.1038/s41746-018-0066-9PMC PMC6550194
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Abstract

Current generation electronic health records suffer a number of problems that make them inefficient and associated with poor clinical satisfaction. Digital scribes or intelligent documentation support systems, take advantage of advances in speech recognition, natural language processing and artificial intelligence, to automate the clinical documentation task currently conducted by humans. Whilst in their infancy, digital scribes are likely to evolve through three broad stages. Human led systems task clinicians with creating documentation, but provide tools to make the task simpler and more effective, for example with dictation support, semantic checking and templates. Mixed-initiative systems are delegated part of the documentation task, converting the conversations in a clinical encounter into summaries suitable for the electronic record. Computer-led systems are delegated full control of documentation and only request human interaction when exceptions are encountered. Intelligent clinical environments permit such augmented clinical encounters to occur in a fully digitised space where the environment becomes the computer. Data from clinical instruments can be automatically transmitted, interpreted using AI and entered directly into the record. Digital scribes raise many issues for clinical practice, including new patient safety risks. Automation bias may see clinicians automatically accept scribe documents without checking. The electronic record also shifts from a human created summary of events to potentially a full audio, video and sensor record of the clinical encounter. Digital scribes promisingly offer a gateway into the clinical workflow for more advanced support for diagnostic, prognostic and therapeutic tasks.

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  • Poor data organization, suboptimal information presentation, and complex interfaces often make it challenging for physicians to identify clinically salient information, while extensive documentation requirements demand continuous attention.

    “Poor data organization, suboptimal information presentation, and complex interfaces often make it challenging for physicians to identify clinically salient information, while extensive documentation requirements demand continuous attention8–10.”

    From When better data meets better design: How EHR data usability and system usability shape physicians' cognitive load (2026) · cites this paper as [9]

    The Abstract (page not provided) says EHRs increase documentation time and make record keeping central to the clinical encounter, supporting the claim about documentation demands. The passages found do not attribute difficulty identifying clinically salient information specifically to poor data organization, suboptimal presentation, or complex interfaces. (LLM, conf=0.84)

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References 30 · Cited by 132

Research works printed but not linked: 28

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Authors
Coiera, E., Kocaballi, B., Halamka, J., & Laranjo, L.
Affiliations
  • Macquarie University (AU)
  • Beth Israel Deaconess Medical Center (US)
Corresponding author
Coiera, Enrico
ORCID
Venue
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Published
2018-10-16
Volume
1
Issue
1
Pages
58-58
ISSN
2398-6352 (ISSN-L)
DOI
10.1038/s41746-018-0066-9
PMC
PMC6550194
OpenAlex
W2897608176
Cited by
132
References
30
Citations by year
2026: 28 · 2025: 28 · 2024: 19 · 2023: 15 · 2022: 12 · 2021: 10 · 2020: 10 · 2019: 8 · 2018: 1 · 2017: 1
FWCI
1.34
Type
journal article
Language
en
Update notices
correction (2018-10-30)
Open access
Licence
Locations
  • npj Digital Medicine (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Department of Health | National Health and Medical Research Council (APP1054146, APP1134919)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
Keywords
clinical documentation automation, speech recognition, natural language processing, electronic health records, mixed-initiative systems, patient safety, automation bias, clinical workflow, clinical decision support

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