Evidence map›Paper›PMID 39312397›Full record

ArticleJMIR AI2024

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

Marieke Meija van Buchem, Ilse M J Kant, Liza King, Jacqueline Kazmaier, Ewout W Steyerberg, Martijn P Bauer

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Marieke Meija van BuchemCAIRELab (Clinical AI Implementation and Research Lab), Leiden University Medical Center, Leiden, Netherlands.ORCID https://orcid.org/0000-0002-2917-0842
Ilse M J KantDepartment of Digital Health, University Medical Center Utrecht, Utrecht, Netherlands.ORCID https://orcid.org/0000-0002-5273-5178
Liza KingAutoscriber B.V., Eindhoven, Netherlands.ORCID https://orcid.org/0009-0008-1536-3046
Jacqueline KazmaierAutoscriber B.V., Eindhoven, Netherlands.ORCID https://orcid.org/0000-0001-6185-5449
Ewout W SteyerbergDepartment of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands.ORCID https://orcid.org/0000-0002-7787-0122
Martijn P BauerDepartment of Internal Medicine, Leiden University Medical Center, Leiden, Netherlands.ORCID https://orcid.org/0000-0002-3710-4271

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AIalgorithmalgorithmsanalyticsartificial intelligenceautomateautomationclinical documentationdeep learningdocumentationdocumentation qualitydocumentation timeimplementationlarge language modellarge language modelsLLMLLMsmachine learningMLmodelmodelsnatural language processingNLPpilot studiespilot studypractical modelpractical models

Identifiers

PMID39312397
PMCPMC11459111

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Registered trials

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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.