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A systematic review of speech recognition technology in health care
Open accessAbstract
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.
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“Strategies to reduce documentation burden, including team documentation, 9-13 automatic speech recognition technologies, 14 and 2021 E&M coding changes 15 have been implemented with variable impact.16”
From Ambient artificial intelligence scribes: utilization and impact on documentation time (2025) · cites this paper as [14]
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Metadata
- Authors
- Johnson, M., Lapkin, S., Long, V., Sanchez, P., Suominen, H., Basilakis, J., & Dawson, L.
- Affiliations
- Ingham Institute (AU)
- Western Sydney University (AU)
- Australian National University (AU)
- University of Wollongong (AU)
- Corresponding author
- Johnson, Maree
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2014-10-28
- Volume
- 14
- Issue
- 1
- Pages
- 94-94
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC4283090
- OpenAlex
- W2046499381
- Cited by
- 141
- References
- 40
- Citations by year
- 2026: 13 · 2025: 17 · 2024: 16 · 2023: 23 · 2022: 18 · 2021: 8 · 2020: 11 · 2019: 7 · 2018: 16 · 2017: 4 · 2016: 2 · 2015: 6
- FWCI
- 0.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Bank (Australian Catholic University) (submittedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- Figshare (submittedVersion)
- ANU Open Research (Australian National University) (submittedVersion, cc-by)
- Funders
- ACT Government
- Australian Government
- Australian National University
- University of New South Wales
- University of Sydney
- University of Western Sydney
- Monash University
- University of Melbourne
- Griffith University
- Queensland University of Technology
- University of Queensland
- Australian Research Council
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Health Sciences › Nursing › Research and Theory › Nursing Diagnosis and Documentation
- Keywords
- speech recognition, clinical documentation, transcription, cost-effectiveness, speech recognition accuracy, voice recognition
- MeSH terms
- Health Services, Humans, Speech Recognition Software
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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