Literature DB >> 16049714

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

C Glaser1, C Trumm, S Nissen-Meyer, M Francke, B Küttner, M Reiser.   

Abstract

With ongoing technical refinements speech recognition systems (SRS) are becoming an increasingly attractive alternative to traditional methods of preparing and transcribing medical reports. The two main components of any SRS are the acoustic model and the language model. Features of modern SRS with continuous speech recognition are macros with individually definable texts and report templates as well as the option to navigate in a text or to control SRS or RIS functions by speech recognition. The best benefit from SRS can be obtained if it is integrated into a RIS/RIS-PACS installation. Report availability and time efficiency of the reporting process (related to recognition rate, time expenditure for editing and correcting a report) are the principal determinants of the clinical performance of any SRS. For practical purposes the recognition rate is estimated by the error rate (unit "word"). Error rates range from 4 to 28%. Roughly 20% of them are errors in the vocabulary which may result in clinically relevant misinterpretation. It is thus mandatory to thoroughly correct any transcribed text as well as to continuously train and adapt the SRS vocabulary. The implementation of SRS dramatically improves report availability. This is most pronounced for CT and CR. However, the individual time expenditure for (SRS-based) reporting increased by 20-25% (CR) and according to literature data there is an increase by 30% for CT and MRI. The extent to which the transcription staff profits from SRS depends largely on its qualification. Online dictation implies a workload shift from the transcription staff to the reporting radiologist.

Entities:  

Mesh:

Year:  2005        PMID: 16049714     DOI: 10.1007/s00117-005-1253-7

Source DB:  PubMed          Journal:  Radiologe        ISSN: 0033-832X            Impact factor:   0.635


  26 in total

1.  Combining speech recognition software with Digital Imaging and Communications in Medicine (DICOM) workstation software on a Microsoft Windows platform.

Authors:  R Ernst; W Carpenter; W Torres; S Wheeler
Journal:  J Digit Imaging       Date:  2001-06       Impact factor: 4.056

2.  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

3.  Continuous speech recognition for clinicians.

Authors:  A Zafar; J M Overhage; C J McDonald
Journal:  J Am Med Inform Assoc       Date:  1999 May-Jun       Impact factor: 4.497

4.  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

5.  Voice recognition.

Authors:  Amit Mehta; Theresa C McLoud
Journal:  J Thorac Imaging       Date:  2003-07       Impact factor: 3.000

Review 6.  Radiology speech recognition: workflow, integration, and productivity issues.

Authors:  Steve Langer
Journal:  Curr Probl Diagn Radiol       Date:  2002 May-Jun

7.  Impact of tightly coupled PACS/speech recognition on report turnaround time in the radiology department.

Authors:  Steve G Langer
Journal:  J Digit Imaging       Date:  2002-03-21       Impact factor: 4.056

8.  A simple error classification system for understanding sources of error in automatic speech recognition and human transcription.

Authors:  Atif Zafar; Burke Mamlin; Susan Perkins; Anne M Belsito; J Marc Overhage; Clement J McDonald
Journal:  Int J Med Inform       Date:  2004-09       Impact factor: 4.046

9.  Radiology reports: assessment of a 5,000-word speech recognizer.

Authors:  A H Robbins; M E Vincent; K Shaffer; R Maietta; M K Srinivasan
Journal:  Radiology       Date:  1988-06       Impact factor: 11.105

10.  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

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  7 in total

1.  Clinical Risk Management in radiology. Part II: applied examples and concluding remarks.

Authors:  M Centonze; D Visconti; S Doratiotto; R Silverio; A Fileni; L Pescarini; R Golfieri
Journal:  Radiol Med       Date:  2010-09-17       Impact factor: 3.469

2.  [Turnaround time for reporting results of radiological examinations in intensive care unit patients: an internal quality control].

Authors:  L Albrecht; R Busse; H Tepe; R Poschmann; U Teichgräber; B Hamm; M de Bucourt
Journal:  Radiologe       Date:  2013-09       Impact factor: 0.635

3.  [Teleradiological report turnaround times: An internal efficiency and quality control analysis].

Authors:  T Seithe; M de Bucourt; T Seithe; R Busse; M Rief; R Doyscher; L Albrecht; H Rathke; M Jonczyk; R Poschmann; H Tepe; B Hamm
Journal:  Radiologe       Date:  2015-05       Impact factor: 0.635

4.  Growing number of emergency cranial CTs in patients with head injury not justified by their clinical need.

Authors:  Lukas Lambert; Ondrej Foltan; Jan Briza; Alena Lambertova; Pavel Harsa; Rohan Banerjee; Jan Danes
Journal:  Wien Klin Wochenschr       Date:  2016-06-20       Impact factor: 1.704

5.  Measuring Consultant Radiologist workload: method and results from a national survey.

Authors:  Adrian P Brady
Journal:  Insights Imaging       Date:  2011-04-21

6.  Analysis of Documentation Speed Using Web-Based Medical Speech Recognition Technology: Randomized Controlled Trial.

Authors:  Markus Vogel; Wolfgang Kaisers; Ralf Wassmuth; Ertan Mayatepek
Journal:  J Med Internet Res       Date:  2015-11-03       Impact factor: 5.428

Review 7.  Radiology reporting-from Hemingway to HAL?

Authors:  Adrian P Brady
Journal:  Insights Imaging       Date:  2018-03-14
  7 in total

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