Evidence map›Paper›PMID 29233669›Full record

Trial reportJournal of biomedical informatics2018

Using voice to create hospital progress notes: Description of a mobile application and supporting system integrated with a commercial electronic health record.

Thomas H Payne, W David Alonso, J Andrew Markiel, Kevin Lybarger, Andrew A White

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of biomedical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

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

  1. Speech recognition for clinical documentation from 1990 to 2018: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019
    Pooled it
  2. Pooled it
  3. Review
  4. Review
  5. Attitudes of optometrists towards artificial intelligence for the diagnosis of retinal disease: A cross-sectional mail-out survey.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2022
    Article
  6. Article
  7. Preferences to improve rounding efficiency amongst hospitalists: a survey analysis.Journal of community hospital internal medicine perspectives · 2021
    Article
  8. Building the evidence-base to reduce electronic health record-related clinician burden.Journal of the American Medical Informatics Association : JAMIA · 2021
    Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Putting the "why" in "EHR": capturing and coding clinical cognition.Journal of the American Medical Informatics Association : JAMIA · 2019
    Article
  14. Article
  15. Article
  16. An Exploration of the Terminology of Clinical Cognition and Reasoning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018
    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

5 authors.

Thomas H PayneDepartment of Medicine, University of Washington, United States; UW Medicine Information Technology Services, Seattle, WA, United States; UW Medicine Center for Scholarship in Patient Care Quality and Safety, Seattle, WA, United States. Electronic address: tpayne@u.washington.edu.
W David AlonsoDepartment of Anesthesiology & Pain Medicine, University of Washington, United States. Electronic address: wdaamd@gmail.com.
J Andrew MarkielUW Medicine Information Technology Services, Seattle, WA, United States. Electronic address: markiel@uw.edu.
Kevin LybargerDepartment of Electrical Engineering, University of Washington, United States. Electronic address: lybarger@uw.edu.
Andrew A WhiteDepartment of Medicine, University of Washington, United States; UW Medicine Center for Scholarship in Patient Care Quality and Safety, Seattle, WA, United States. Electronic address: andwhite@u.washington.edu.

Funding

Improving Accuracy of Electronic Notes Using A Faster, Simpler ApproachR21HS023631 · AHRQ · UNIVERSITY OF WASHINGTON · PI PAYNE, THOMAS H. · 2014 to 2015
$294k
AHRQ HHS R21 HS023631
6 · The paper itself

Abstract

We describe the development and design of a smartphone app-based system to create inpatient progress notes using voice, commercial automatic speech recognition software, with text processing to recognize spoken voice commands and format the note, and integration with a commercial EHR. This new system fits hospital rounding workflow and was used to support a randomized clinical trial testing whether use of voice to create notes improves timeliness of note availability, note quality, and physician satisfaction with the note creation process. The system was used to create 709 notes which were placed in the corresponding patient's EHR record. The median time from pressing the Send button to appearance of the formatted note in the Inbox was 8.8 min. It was generally very reliable, accepted by physician users, and secure. This approach provides an alternative to use of keyboard and templates to create progress notes and may appeal to physicians who prefer voice to typing.

Indexed as

Speech Recognition SoftwareData AccuracyDocumentationElectronic Health RecordsHumansMedical RecordsMobile ApplicationsPhysiciansPractice Patterns, Physicians'User-Computer InterfaceWorkflowAutomatic speech recognitionElectronic health recordsHospital carePhysician documentationWorkflow

Identifiers

PMID29233669
PMCPMC12974612

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.