ArticleJMIR AI2024
Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study.
Article in JMIR AI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Application of artificial intelligence tools and clinical documentation burden: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Evaluating the performance of artificial intelligence-based speech recognition for clinical documentation: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- For the record: a narrative review of ambient voice technology in clinical documentation for dentistry and wider healthcare.British dental journal · 2026Review
- Article
- Review
- Evaluating the impact of artificial intelligence scribes on clinical documentation in primary care: a simulation study.JAMIA open · 2026Article
- Evaluation of Socio-Technical Mechanisms Shaping AI Scribe Documentation Failures: A Netnographic Study.Journal of evaluation in clinical practice · 2026Article
- Evaluation of an AI Medical Scribe After 236,153 Notes Generated Across Care Levels in a European Health System: Mixed Methods Retrospective Observational Study.JMIR medical informatics · 2026Observational
- Feasibility of Tailoring Artificial Intelligence-Assisted Ambient Scribes for Intensive Care Unit Rounds: Algorithm Development and Validation.JMIR medical informatics · 2026Article
- Implementation outcomes of AI documentation support in routine clinical practice: A non-randomized controlled trial.PLOS digital health · 2026Article
- AI-Assisted Medical Documentation in a Multilingual Swiss Health Care System: Proof-of-Concept Study.JMIR AI · 2026Article
- Evaluation of an AI Scribe Tool in the Emergency Department: A Single-Arm Observational Study.Emergency medicine Australasia : EMA · 2026Observational
- Assessing the Reliability, Accuracy, and Relevance of Artificial Intelligence Speech Recognition for Clinical Documentation: A Scoping Review.Journal of evaluation in clinical practice · 2026Article
- Large language model-based scribing tools for ophthalmology: performance and safety evaluation using simulated clinical encounters.Eye (London, England) · 2026Article
- Validating large language model-assisted data extraction from clinical notes.ESMO real world data and digital oncology · 2026Article
- From Clinical Encounter to Draft Documentation: A Mechanistic Narrative Review of Ambient Scribe Technology.Cureus · 2026Review
- The Regional Implementation of an Electronic Health Record-Integrated Ambient Scribe in Primary and Secondary Care in England: Real-Time Qualitative Evaluation.JMIR medical informatics · 2026Article
- A Language Model for Pediatric Occupational Therapy Documentation: Model Development and Pilot Study.JMIR AI · 2026Article
- Developing an AI platform for voice-input medical records in Polish: a digital application for optimisation of physicians' workload.Scientific reports · 2026Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPhysicians spend approximately half of their time on administrative tasks, which is one of the leading causes of physician burnout and decreased work satisfaction. The implementation of natural language processing-assisted clinical documentation tools may provide a solution.
objectiveThis study investigates the impact of a commercially available Dutch digital scribe system on clinical documentation efficiency and quality.
methodsMedical students with experience in clinical practice and documentation (n=22) created a total of 430 summaries of mock consultations and recorded the time they spent on this task. The consultations were summarized using 3 methods: manual summaries, fully automated summaries, and automated summaries with manual editing. We then randomly reassigned the summaries and evaluated their quality using a modified version of the Physician Documentation Quality Instrument (PDQI-9). We compared the differences between the 3 methods in descriptive statistics, quantitative text metrics (word count and lexical diversity), the PDQI-9, Recall-Oriented Understudy for Gisting Evaluation scores, and BERTScore.
resultsThe median time for manual summarization was 202 seconds against 186 seconds for editing an automatic summary. Without editing, the automatic summaries attained a poorer PDQI-9 score than manual summaries (median PDQI-9 score 25 vs 31, P<.001, ANOVA test). Automatic summaries were found to have higher word counts but lower lexical diversity than manual summaries (P<.001, independent t test). The study revealed variable impacts on PDQI-9 scores and summarization time across individuals. Generally, students viewed the digital scribe system as a potentially useful tool, noting its ease of use and time-saving potential, though some criticized the summaries for their greater length and rigid structure.
conclusionsThis study highlights the potential of digital scribes in improving clinical documentation processes by offering a first summary draft for physicians to edit, thereby reducing documentation time without compromising the quality of patient records. Furthermore, digital scribes may be more beneficial to some physicians than to others and could play a role in improving the reusability of clinical documentation. Future studies should focus on the impact and quality of such a system when used by physicians in clinical practice.
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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.