Evidence map›Paper›PMID 25336589›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2015

Capturing patient information at nursing shift changes: methodological evaluation of speech recognition and information extraction.

Hanna Suominen, Maree Johnson, Liyuan Zhou, Paula Sanchez, Raul Sirel, Jim Basilakis, Leif Hanlen, Dominique Estival, Linda Dawson, Barbara Kelly

Open access · bronzeAbstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
16.4field-weighted citation impact, top 1% of its field
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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 citations in OpenAlex.

  1. Pooled it
  2. 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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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

10 authors at 9 institutions in 2 countries.

Hanna SuominenMachine Learning Research Group, NICTA, College of Engineering and Computer Science, The Australian National University, Faculty of Health, University of Canberra, and Department of Information Technology, University of Turku, Canberra, Australian Capital Territory, Australia.
Maree JohnsonResearch Faculty of Health Sciences, Australian Catholic University, Sydney, New South Wales, Australia.
Liyuan ZhouMachine Learning Research Group, NICTA, Canberra, Australian Capital Territory, Australia.
Paula SanchezCentre for Applied Nursing Research (University of Western Sydney and South Western Sydney Local Health District), Sydney, New South Wales, Australia.
Raul SirelInstitute of Estonian and General Linguistics, University of Tartu, Tartu, Estonia.
Jim BasilakisSchool of Computing, Engineering and Mathematics, University of Western Sydney, Sydney, New South Wales, Australia.
Leif HanlenMachine Learning Research Group, NICTA, College of Engineering and Computer Science, The Australian National University, Faculty of Health, University of Canberra, Canberra, Australian Capital Territory, Australia.
Dominique EstivalThe MARCS Institute, University of Western Sydney and Department of Linguistics, University of Sydney, Sydney, New South Wales, Australia.
Linda DawsonFaculty of Social Sciences, University of Wollongong, Wollongong, New South Wales, Australia.
Barbara KellySchool of Languages and Linguistics, The University of Melbourne, Melbourne, Victoria, Australia.
Australian National University · AUACT GovernmentAustralian Catholic University · AUSouth Western Sydney Local Health District · AUUniversity of Melbourne · AUUniversity of Sydney · AUUniversity of Tartu · EEUniversity of Wollongong · AUWestern Sydney University · AU

Funding

NIGMS NIH HHS GM10331601
6 · The paper itself

Abstract

objectiveWe study the use of speech recognition and information extraction to generate drafts of Australian nursing-handover documents.

methodsSpeech recognition correctness and clinicians' preferences were evaluated using 15 recorder-microphone combinations, six documents, three speakers, Dragon Medical 11, and five survey/interview participants. Information extraction correctness evaluation used 260 documents, six-class classification for each word, two annotators, and the CRF++ conditional random field toolkit.

resultsA noise-cancelling lapel-microphone with a digital voice recorder gave the best correctness (79%). This microphone was also the most preferred option by all but one participant. Although the participants liked the small size of this recorder, their preference was for tablets that can also be used for document proofing and sign-off, among other tasks. Accented speech was harder to recognize than native language and a male speaker was detected better than a female speaker. Information extraction was excellent in filtering out irrelevant text (85% F1) and identifying text relevant to two classes (87% and 70% F1). Similarly to the annotators' disagreements, there was confusion between the remaining three classes, which explains the modest 62% macro-averaged F1. DISCUSSION: We present evidence for the feasibility of speech recognition and information extraction to support clinicians' in entering text and unlock its content for computerized decision-making and surveillance in healthcare.

conclusionsThe benefits of this automation include storing all information; making the drafts available and accessible almost instantly to everyone with authorized access; and avoiding information loss, delays, and misinterpretations inherent to using a ward clerk or transcription services.

Indexed as

Patient HandoffSpeech Recognition SoftwareAustraliaDecision Support Systems, ClinicalFeasibility StudiesFemaleHumansMaleNursing Processcomputer systems evaluationinformation extractionnursing recordspatient handoffspeech recognition software

Identifiers

PMID25336589
PMCPMC5901121
OpenAlexW2125883684

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.