Cargando…

A systematic review of speech recognition technology in health care

BACKGROUND: To undertake a systematic review of existing literature relating to speech recognition technology and its application within health care. METHODS: A systematic review of existing literature from 2000 was undertaken. Inclusion criteria were: all papers that referred to speech recognition...

Descripción completa

Detalles Bibliográficos
Autores principales: Johnson, Maree, Lapkin, Samuel, Long, Vanessa, Sanchez, Paula, Suominen, Hanna, Basilakis, Jim, Dawson, Linda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4283090/
https://www.ncbi.nlm.nih.gov/pubmed/25351845
http://dx.doi.org/10.1186/1472-6947-14-94
_version_ 1782351215322988544
author Johnson, Maree
Lapkin, Samuel
Long, Vanessa
Sanchez, Paula
Suominen, Hanna
Basilakis, Jim
Dawson, Linda
author_facet Johnson, Maree
Lapkin, Samuel
Long, Vanessa
Sanchez, Paula
Suominen, Hanna
Basilakis, Jim
Dawson, Linda
author_sort Johnson, Maree
collection PubMed
description BACKGROUND: To undertake a systematic review of existing literature relating to speech recognition technology and its application within health care. METHODS: A systematic review of existing literature from 2000 was undertaken. Inclusion criteria were: all papers that referred to speech recognition (SR) in health care settings, used by health professionals (allied health, medicine, nursing, technical or support staff), with an evaluation or patient or staff outcomes. Experimental and non-experimental designs were considered. Six databases (Ebscohost including CINAHL, EMBASE, MEDLINE including the Cochrane Database of Systematic Reviews, OVID Technologies, PreMED-LINE, PsycINFO) were searched by a qualified health librarian trained in systematic review searches initially capturing 1,730 references. Fourteen studies met the inclusion criteria and were retained. RESULTS: The heterogeneity of the studies made comparative analysis and synthesis of the data challenging resulting in a narrative presentation of the results. SR, although not as accurate as human transcription, does deliver reduced turnaround times for reporting and cost-effective reporting, although equivocal evidence of improved workflow processes. CONCLUSIONS: SR systems have substantial benefits and should be considered in light of the cost and selection of the SR system, training requirements, length of the transcription task, potential use of macros and templates, the presence of accented voices or experienced and in-experienced typists, and workflow patterns.
format Online
Article
Text
id pubmed-4283090
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-42830902015-01-06 A systematic review of speech recognition technology in health care Johnson, Maree Lapkin, Samuel Long, Vanessa Sanchez, Paula Suominen, Hanna Basilakis, Jim Dawson, Linda BMC Med Inform Decis Mak Research Article BACKGROUND: To undertake a systematic review of existing literature relating to speech recognition technology and its application within health care. METHODS: A systematic review of existing literature from 2000 was undertaken. Inclusion criteria were: all papers that referred to speech recognition (SR) in health care settings, used by health professionals (allied health, medicine, nursing, technical or support staff), with an evaluation or patient or staff outcomes. Experimental and non-experimental designs were considered. Six databases (Ebscohost including CINAHL, EMBASE, MEDLINE including the Cochrane Database of Systematic Reviews, OVID Technologies, PreMED-LINE, PsycINFO) were searched by a qualified health librarian trained in systematic review searches initially capturing 1,730 references. Fourteen studies met the inclusion criteria and were retained. RESULTS: The heterogeneity of the studies made comparative analysis and synthesis of the data challenging resulting in a narrative presentation of the results. SR, although not as accurate as human transcription, does deliver reduced turnaround times for reporting and cost-effective reporting, although equivocal evidence of improved workflow processes. CONCLUSIONS: SR systems have substantial benefits and should be considered in light of the cost and selection of the SR system, training requirements, length of the transcription task, potential use of macros and templates, the presence of accented voices or experienced and in-experienced typists, and workflow patterns. BioMed Central 2014-10-28 /pmc/articles/PMC4283090/ /pubmed/25351845 http://dx.doi.org/10.1186/1472-6947-14-94 Text en © Johnson et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Johnson, Maree
Lapkin, Samuel
Long, Vanessa
Sanchez, Paula
Suominen, Hanna
Basilakis, Jim
Dawson, Linda
A systematic review of speech recognition technology in health care
title A systematic review of speech recognition technology in health care
title_full A systematic review of speech recognition technology in health care
title_fullStr A systematic review of speech recognition technology in health care
title_full_unstemmed A systematic review of speech recognition technology in health care
title_short A systematic review of speech recognition technology in health care
title_sort systematic review of speech recognition technology in health care
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4283090/
https://www.ncbi.nlm.nih.gov/pubmed/25351845
http://dx.doi.org/10.1186/1472-6947-14-94
work_keys_str_mv AT johnsonmaree asystematicreviewofspeechrecognitiontechnologyinhealthcare
AT lapkinsamuel asystematicreviewofspeechrecognitiontechnologyinhealthcare
AT longvanessa asystematicreviewofspeechrecognitiontechnologyinhealthcare
AT sanchezpaula asystematicreviewofspeechrecognitiontechnologyinhealthcare
AT suominenhanna asystematicreviewofspeechrecognitiontechnologyinhealthcare
AT basilakisjim asystematicreviewofspeechrecognitiontechnologyinhealthcare
AT dawsonlinda asystematicreviewofspeechrecognitiontechnologyinhealthcare
AT johnsonmaree systematicreviewofspeechrecognitiontechnologyinhealthcare
AT lapkinsamuel systematicreviewofspeechrecognitiontechnologyinhealthcare
AT longvanessa systematicreviewofspeechrecognitiontechnologyinhealthcare
AT sanchezpaula systematicreviewofspeechrecognitiontechnologyinhealthcare
AT suominenhanna systematicreviewofspeechrecognitiontechnologyinhealthcare
AT basilakisjim systematicreviewofspeechrecognitiontechnologyinhealthcare
AT dawsonlinda systematicreviewofspeechrecognitiontechnologyinhealthcare