Evidence map›Paper›PMID 30332689›Full record

ArticleApplied clinical informatics2018

Asynchronous Speech Recognition Affects Physician Editing of Notes.

Kevin J Lybarger, Mari Ostendorf, Eve Riskin, Thomas H Payne, Andrew A White, Meliha Yetisgen

Abstract read
In one paragraph

Article in Applied clinical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
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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.

Kevin J LybargerDepartment of Electrical Engineering, University of Washington, Seattle, Washington, United States.
Mari OstendorfDepartment of Electrical Engineering, University of Washington, Seattle, Washington, United States.
Eve RiskinDepartment of Electrical Engineering, University of Washington, Seattle, Washington, United States.
Thomas H PayneDivision of General Internal Medicine, University of Washington, Seattle, Washington, United States.
Andrew A WhiteDivision of General Internal Medicine, University of Washington, Seattle, Washington, United States.
Meliha YetisgenDepartment of Biomedical & Health Informatics, University of Washington, Seattle, Washington, United States.

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

objectiveClinician progress notes are an important record for care and communication, but there is a perception that electronic notes take too long to write and may not accurately reflect the patient encounter, threatening quality of care. Automatic speech recognition (ASR) has the potential to improve clinical documentation process; however, ASR inaccuracy and editing time are barriers to wider use. We hypothesized that automatic text processing technologies could decrease editing time and improve note quality. To inform the development of these technologies, we studied how physicians create clinical notes using ASR and analyzed note content that is revised or added during asynchronous editing. MATERIALS AND

methodsWe analyzed a corpus of 649 dictated clinical notes from 9 physicians. Notes were dictated during rounds to portable devices, automatically transcribed, and edited later at the physician's convenience. Comparing ASR transcripts and the final edited notes, we identified the word sequences edited by physicians and categorized the edits by length and content.

resultsWe found that 40% of the words in the final notes were added by physicians while editing: 6% corresponded to short edits associated with error correction and format changes, and 34% were associated with longer edits. Short error correction edits that affect note accuracy are estimated to be less than 3% of the words in the dictated notes. Longer edits primarily involved insertion of material associated with clinical data or assessment and plans. The longer edits improve note completeness; some could be handled with verbalized commands in dictation.

conclusionProcess interventions to reduce ASR documentation burden, whether related to technology or the dictation/editing workflow, should apply a portfolio of solutions to address all categories of required edits. Improved processes could reduce an important barrier to broader use of ASR by clinicians and improve note quality.

Indexed as

Electronic Health RecordsPhysiciansSpeech Recognition SoftwareHumans

Identifiers

PMID30332689
PMCPMC6192791

What OpenQuestion holds

Textmetadata
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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.