Evidence map›Paper›PMID 27435949›Full record

ArticleInternational journal of medical informatics2016

Incidence of speech recognition errors in the emergency department.

Foster R Goss, Li Zhou, Scott G Weiner

Abstract read
In one paragraph

Article in International journal of medical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 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, 1 synthesis or guideline 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
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  16. Incidence of speech recognition errors in the emergency department.International journal of medical informatics · 2016
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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

3 authors.

Foster R GossUniversity of Colorado, Department of Emergency Medicine, Aurora, CO, United States; Tufts Medical Center, Department of Emergency Medicine and Clinical Decision Making, Boston, MA, United States. Electronic address: Foster.Goss@ucdenver.edu.
Li ZhouDivision of General Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, United States; Clinical & Quality Analysis, Partners HealthCare System, Boston, MA, United States; Clinical Informatics, Partners eCare, Partners HealthCare System, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
Scott G WeinerDivision of Health Policy Translation, Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, United States.

Funding

NLP to Improve Accuracy and Quality of Dictated Medical DocumentsR01HS024264 · AHRQ · BRIGHAM AND WOMEN'S HOSPITAL · PI ZHOU, LI · 2015 to 2017
$750k
AHRQ HHS R01 HS024264
6 · The paper itself

Abstract

backgroundPhysician use of computerized speech recognition (SR) technology has risen in recent years due to its ease of use and efficiency at the point of care. However, error rates between 10 and 23% have been observed, raising concern about the number of errors being entered into the permanent medical record, their impact on quality of care and medical liability that may arise. Our aim was to determine the incidence and types of SR errors introduced by this technology in the emergency department (ED).

settingLevel 1 emergency department with 42,000 visits/year in a tertiary academic teaching hospital.

methodsA random sample of 100 notes dictated by attending emergency physicians (EPs) using SR software was collected from the ED electronic health record between January and June 2012. Two board-certified EPs annotated the notes and conducted error analysis independently. An existing classification schema was adopted to classify errors into eight errors types. Critical errors deemed to potentially impact patient care were identified.

resultsThere were 128 errors in total or 1.3 errors per note, and 14.8% (n=19) errors were judged to be critical. 71% of notes contained errors, and 15% contained one or more critical errors. Annunciation errors were the highest at 53.9% (n=69), followed by deletions at 18.0% (n=23) and added words at 11.7% (n=15). Nonsense errors, homonyms and spelling errors were present in 10.9% (n=14), 4.7% (n=6), and 0.8% (n=1) of notes, respectively. There were no suffix or dictionary errors. Inter-annotator agreement was 97.8%.

conclusionsThis is the first estimate at classifying speech recognition errors in dictated emergency department notes. Speech recognition errors occur commonly with annunciation errors being the most frequent. Error rates were comparable if not lower than previous studies. 15% of errors were deemed critical, potentially leading to miscommunication that could affect patient care.

Indexed as

DocumentationElectronic Health RecordsEmergency Service, HospitalSpeechHumansIncidenceMedical ErrorsPhysiciansSpeech Recognition SoftwareEmergency medicinePatient safetySpeech recognition

Identifiers

PMID27435949
PMCPMC7263796

What OpenQuestion holds

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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.