A systematic review of speech recognition technology in health care

Maree Johnson, Samuel Lapkin, Vanessa Long, Paula Sanchez, Hanna SUOMINEN, Jim Basilakis, Linda Dawson

    Research output: Contribution to journalArticle

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    Abstract

    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.
    Original languageEnglish
    Pages (from-to)1-14
    Number of pages14
    JournalBMC Medical Informatics and Decision Making
    Volume14
    Issue number94
    DOIs
    Publication statusPublished - 2014

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    Biomedical Technology
    Allied Health Personnel
    Workflow
    Databases
    Librarians
    Technology
    Delivery of Health Care
    Costs and Cost Analysis
    MEDLINE
    Nursing
    Medicine
    Health

    Cite this

    Johnson, M., Lapkin, S., Long, V., Sanchez, P., SUOMINEN, H., Basilakis, J., & Dawson, L. (2014). A systematic review of speech recognition technology in health care. BMC Medical Informatics and Decision Making, 14(94), 1-14. https://doi.org/10.1186/1472-6947-14-94
    Johnson, Maree ; Lapkin, Samuel ; Long, Vanessa ; Sanchez, Paula ; SUOMINEN, Hanna ; Basilakis, Jim ; Dawson, Linda. / A systematic review of speech recognition technology in health care. In: BMC Medical Informatics and Decision Making. 2014 ; Vol. 14, No. 94. pp. 1-14.
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    abstract = "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 systematicreview 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 presenceof accented voices or experienced and in-experienced typists, and workflow patterns.",
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    author = "Maree Johnson and Samuel Lapkin and Vanessa Long and Paula Sanchez and Hanna SUOMINEN and Jim Basilakis and Linda Dawson",
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    Johnson, M, Lapkin, S, Long, V, Sanchez, P, SUOMINEN, H, Basilakis, J & Dawson, L 2014, 'A systematic review of speech recognition technology in health care', BMC Medical Informatics and Decision Making, vol. 14, no. 94, pp. 1-14. https://doi.org/10.1186/1472-6947-14-94

    A systematic review of speech recognition technology in health care. / Johnson, Maree; Lapkin, Samuel; Long, Vanessa; Sanchez, Paula ; SUOMINEN, Hanna; Basilakis, Jim; Dawson, Linda.

    In: BMC Medical Informatics and Decision Making, Vol. 14, No. 94, 2014, p. 1-14.

    Research output: Contribution to journalArticle

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    AU - Johnson, Maree

    AU - Lapkin, Samuel

    AU - Long, Vanessa

    AU - Sanchez, Paula

    AU - SUOMINEN, Hanna

    AU - Basilakis, Jim

    AU - Dawson, Linda

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    AB - 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 systematicreview 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 presenceof accented voices or experienced and in-experienced typists, and workflow patterns.

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    KW - interactive voice response systems

    KW - human transcriptions

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