Literature DB >> 12741898

Comparison of voice-automated transcription and human transcription in generating pathology reports.

Maamoun M Al-Aynati1, Katherine A Chorneyko.   

Abstract

CONTEXT: Software that can convert spoken words into written text has been available since the early 1980s. Early continuous speech systems were developed in 1994, with the latest commercially available editions having a claimed accuracy of up to 98% of speech recognition at natural speech rates.
OBJECTIVES: To evaluate the efficacy of one commercially available voice-recognition software system with pathology vocabulary in generating pathology reports and to compare this with human transcription. To draw cost analysis conclusions regarding human versus computer-based transcription.
DESIGN: Two hundred six routine pathology reports from the surgical pathology material handled at St Joseph's Healthcare, Hamilton, Ontario, were generated simultaneously using computer-based transcription and human transcription. The following hardware and software were used: a desktop 450-MHz Intel Pentium III processor with 192 MB of RAM, a speech-quality sound card (Sound Blaster), noise-canceling headset microphone, and IBM ViaVoice Pro version 8 with pathology vocabulary support (Voice Automated, Huntington Beach, Calif). The cost of the hardware and software used was approximately Can 2250 dollars.
RESULTS: A total of 23 458 words were transcribed using both methods with a mean of 114 words per report. The mean accuracy rate was 93.6% (range, 87.4%-96%) using the computer software, compared to a mean accuracy of 99.6% (range, 99.4%-99.8%) for human transcription (P <.001). Time needed to edit documents by the primary evaluator (M.A.) using the computer was on average twice that needed for editing the documents produced by human transcriptionists (range, 1.4-3.5 times). The extra time needed to edit documents was 67 minutes per week (13 minutes per day).
CONCLUSIONS: Computer-based continuous speech-recognition systems in pathology can be successfully used in pathology practice even during the handling of gross pathology specimens. The relatively low accuracy rate of this voice-recognition software with resultant increased editing burden on pathologists may not encourage its application on a wide scale in pathology departments with sufficient human transcription services, despite significant potential financial savings. However, computer-based transcription represents an attractive and relatively inexpensive alternative to human transcription in departments where there is a shortage of transcription services, and will no doubt become more commonly used in pathology departments in the future.

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Mesh:

Year:  2003        PMID: 12741898     DOI: 10.5858/2003-127-721-COVTAH

Source DB:  PubMed          Journal:  Arch Pathol Lab Med        ISSN: 0003-9985            Impact factor:   5.534


  11 in total

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Authors:  Keith S White
Journal:  Pediatr Radiol       Date:  2005-05-18

2.  Improving the utility of speech recognition through error detection.

Authors:  Kimberly Voll; Stella Atkins; Bruce Forster
Journal:  J Digit Imaging       Date:  2008-12       Impact factor: 4.056

Review 3.  Electronic Health Record Interactions through Voice: A Review.

Authors:  Yaa A Kumah-Crystal; Claude J Pirtle; Harrison M Whyte; Edward S Goode; Shilo H Anders; Christoph U Lehmann
Journal:  Appl Clin Inform       Date:  2018-07-18       Impact factor: 2.342

4.  A usability framework for speech recognition technologies in clinical handover: a pre-implementation study.

Authors:  Linda Dawson; Maree Johnson; Hanna Suominen; Jim Basilakis; Paula Sanchez; Dominique Estival; Barbara Kelly; Leif Hanlen
Journal:  J Med Syst       Date:  2014-05-15       Impact factor: 4.460

5.  Digital dictation and voice transcription software enhances outpatient clinic letter production: a crossover study.

Authors:  Kinesh Patel; Marcus Harbord
Journal:  Frontline Gastroenterol       Date:  2012-04-24

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

Authors:  Hanna Suominen; Maree Johnson; Liyuan Zhou; Paula Sanchez; Raul Sirel; Jim Basilakis; Leif Hanlen; Dominique Estival; Linda Dawson; Barbara Kelly
Journal:  J Am Med Inform Assoc       Date:  2014-10-21       Impact factor: 4.497

Review 7.  Risks and benefits of speech recognition for clinical documentation: a systematic review.

Authors:  Tobias Hodgson; Enrico Coiera
Journal:  J Am Med Inform Assoc       Date:  2015-11-17       Impact factor: 4.497

8.  Benchmarking clinical speech recognition and information extraction: new data, methods, and evaluations.

Authors:  Hanna Suominen; Liyuan Zhou; Leif Hanlen; Gabriela Ferraro
Journal:  JMIR Med Inform       Date:  2015-04-27

Review 9.  A systematic review of speech recognition technology in health care.

Authors:  Maree Johnson; Samuel Lapkin; Vanessa Long; Paula Sanchez; Hanna Suominen; Jim Basilakis; Linda Dawson
Journal:  BMC Med Inform Decis Mak       Date:  2014-10-28       Impact factor: 2.796

Review 10.  Digital scribe utility and barriers to implementation in clinical practice: a scoping review.

Authors:  Shilpa Ghatnekar; Adam Faletsky; Vinod E Nambudiri
Journal:  Health Technol (Berl)       Date:  2021-06-02
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