Literature DB >> 10332653

Continuous speech recognition for clinicians.

A Zafar1, J M Overhage, C J McDonald.   

Abstract

The current generation of continuous speech recognition systems claims to offer high accuracy (greater than 95 percent) speech recognition at natural speech rates (150 words per minute) on low-cost (under $2000) platforms. This paper presents a state-of-the-technology summary, along with insights the authors have gained through testing one such product extensively and other products superficially. The authors have identified a number of issues that are important in managing accuracy and usability. First, for efficient recognition users must start with a dictionary containing the phonetic spellings of all words they anticipate using. The authors dictated 50 discharge summaries using one inexpensive internal medicine dictionary ($30) and found that they needed to add an additional 400 terms to get recognition rates of 98 percent. However, if they used either of two more expensive and extensive commercial medical vocabularies ($349 and $695), they did not need to add terms to get a 98 percent recognition rate. Second, users must speak clearly and continuously, distinctly pronouncing all syllables. Users must also correct errors as they occur, because accuracy improves with error correction by at least 5 percent over two weeks. Users may find it difficult to train the system to recognize certain terms, regardless of the amount of training, and appropriate substitutions must be created. For example, the authors had to substitute "twice a day" for "bid" when using the less expensive dictionary, but not when using the other two dictionaries. From trials they conducted in settings ranging from an emergency room to hospital wards and clinicians' offices, they learned that ambient noise has minimal effect. Finally, they found that a minimal "usable" hardware configuration (which keeps up with dictation) comprises a 300-MHz Pentium processor with 128 MB of RAM and a "speech quality" sound card (e.g., SoundBlaster, $99). Anything less powerful will result in the system lagging behind the speaking rate. The authors obtained 97 percent accuracy with just 30 minutes of training when using the latest edition of one of the speech recognition systems supplemented by a commercial medical dictionary. This technology has advanced considerably in recent years and is now a serious contender to replace some or all of the increasingly expensive alternative methods of dictation with human transcription.

Entities:  

Mesh:

Year:  1999        PMID: 10332653      PMCID: PMC61360          DOI: 10.1136/jamia.1999.0060195

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


  9 in total

1.  Voice recognition for the radiology market.

Authors:  R A Reed
Journal:  Top Health Rec Manage       Date:  1992-03

2.  Computer-based speech recognition as a replacement for medical transcription.

Authors:  D I Rosenthal; F S Chew; D E Dupuy; S V Kattapuram; W E Palmer; R M Yap; L A Levine
Journal:  AJR Am J Roentgenol       Date:  1998-01       Impact factor: 3.959

3.  What does voice-processing technology support today?

Authors:  R Nakatsu; Y Suzuki
Journal:  Proc Natl Acad Sci U S A       Date:  1995-10-24       Impact factor: 11.205

4.  State of the art in continuous speech recognition.

Authors:  J Makhoul; R Schwartz
Journal:  Proc Natl Acad Sci U S A       Date:  1995-10-24       Impact factor: 11.205

5.  A continuous-speech interface to a decision support system: II. An evaluation using a Wizard-of-Oz experimental paradigm.

Authors:  W M Detmer; S Shiffman; J C Wyatt; C P Friedman; C D Lane; L M Fagan
Journal:  J Am Med Inform Assoc       Date:  1995 Jan-Feb       Impact factor: 4.497

6.  Computerized radiologic reporting with voice data-entry.

Authors:  B W Leeming; D Porter; J D Jackson; H L Bleich; M Simon
Journal:  Radiology       Date:  1981-03       Impact factor: 11.105

7.  Advances in radiologic reporting with Computerized Language Information Processing (CLIP).

Authors:  B W Leeming; M Simon; J D Jackson; G L Horowitz; H L Bleich
Journal:  Radiology       Date:  1979-11       Impact factor: 11.105

8.  Implementation of a comprehensive computer-based patient record system in Kaiser Permanente's Northwest Region.

Authors:  H L Chin; M Krall
Journal:  MD Comput       Date:  1997 Jan-Feb

9.  Development of a controlled medical terminology: knowledge acquisition and knowledge representation.

Authors:  M A Musen; K E Wieckert; E T Miller; K E Campbell; L M Fagan
Journal:  Methods Inf Med       Date:  1995-03       Impact factor: 2.176

  9 in total
  22 in total

1.  A Java speech implementation of the Mini Mental Status Exam.

Authors:  S S Wang; J Starren
Journal:  Proc AMIA Symp       Date:  1999

2.  Clictate: a computer-based documentation tool for guideline-based care.

Authors:  Kevin B Johnson; John Cowan
Journal:  J Med Syst       Date:  2002-02       Impact factor: 4.460

3.  Comparative evaluation of three continuous speech recognition software packages in the generation of medical reports.

Authors:  E G Devine; S A Gaehde; A C Curtis
Journal:  J Am Med Inform Assoc       Date:  2000 Sep-Oct       Impact factor: 4.497

4.  Using natural language processing to analyze physician modifications to data entry templates.

Authors:  Adam B Wilcox; Scott P Narus; Watson A Bowes
Journal:  Proc AMIA Symp       Date:  2002

5.  Speech recognition interface to a hospital information system using a self-designed visual basic program: initial experience.

Authors:  Edward C Callaway; Clifford F Sweet; Eliot Siegel; John M Reiser; Douglas P Beall
Journal:  J Digit Imaging       Date:  2002-04-30       Impact factor: 4.056

6.  Speech recognition as a transcription aid: a randomized comparison with standard transcription.

Authors:  David N Mohr; David W Turner; Gregory R Pond; Joseph S Kamath; Cathy B De Vos; Paul C Carpenter
Journal:  J Am Med Inform Assoc       Date:  2003 Jan-Feb       Impact factor: 4.497

7.  Impacts of computerized physician documentation in a teaching hospital: perceptions of faculty and resident physicians.

Authors:  Peter J Embi; Thomas R Yackel; Judith R Logan; Judith L Bowen; Thomas G Cooney; Paul N Gorman
Journal:  J Am Med Inform Assoc       Date:  2004-04-02       Impact factor: 4.497

Review 8.  Speech recognition implementation in radiology.

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

Review 9.  [Speech recognition: impact on workflow and report availability].

Authors:  C Glaser; C Trumm; S Nissen-Meyer; M Francke; B Küttner; M Reiser
Journal:  Radiologe       Date:  2005-08       Impact factor: 0.635

10.  Clinical computing in general dentistry.

Authors:  Titus K L Schleyer; Thankam P Thyvalikakath; Heiko Spallek; Miguel H Torres-Urquidy; Pedro Hernandez; Jeannie Yuhaniak
Journal:  J Am Med Inform Assoc       Date:  2006-02-24       Impact factor: 4.497

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