Evidence map›Paper›PMID 41483142›Full record

ArticleJournal of medical systems2026

The Current State of Digital Scribes in Primary Care: A Scoping Review.

Rajesh Nair, Muhammad Moinuddin Hashmi, Sameer S Kassim, Alexander Singer

Abstract readScoping Review
PubMed Publisher
In one paragraph

Article in Journal of medical systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Article
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

4 authors.

Rajesh NairHealth Information Science, University of Victoria, Victoria, BC, Canada. rajeshnair@uvic.ca.
Muhammad Moinuddin HashmiDepartment of Family Medicine, University of Manitoba, Winnipeg, MB, Canada.
Sameer S KassimDepartment of Family Medicine, University of Manitoba, Winnipeg, MB, Canada.
Alexander SingerDepartment of Family Medicine, University of Manitoba, Winnipeg, MB, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

DocumentationElectronic Health RecordsNatural Language ProcessingPrimary Health CareSpeech Recognition SoftwareArtificial IntelligenceHumansWorkflowartificial intelligence scribesautomatic speech recognitiondigital scribeselectronic scribesnatural language processingprimary carescoping review

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.