Literature DB >> 19027683

Frequency and spectrum of errors in final radiology reports generated with automatic speech recognition technology.

Leslie E Quint1, Douglas J Quint, James D Myles.   

Abstract

PURPOSE: Automatic speech recognition technology has a high frequency of transcription errors, necessitating careful proofreading and report editing. The purpose of this study was to determine the frequency and spectrum of significant dictation errors in finalized radiology reports generated with speech recognition technology.
METHODS: All 265 radiology reports that were reviewed in preparation for 12 consecutive weekly multidisciplinary thoracic oncology group conferences were examined for significant dictation errors; reports were compared with the corresponding imaging studies. In addition, departmental radiologists were surveyed regarding their estimates of overall and individual report error rates.
RESULTS: Two hundred six of 265 (78%) reports contained no significant errors, and 59 (22%) contained errors. Report error rates by individual radiologists ranged from 0% to 100%. There were no significant differences in error rates between native and nonnative English speakers (P > .8) or between reports dictated by faculty members alone and those dictated by trainees and signed by faculty members (P > .3). The most frequent types of errors were wrong-word substitution, nonsense phrases, and missing words. Fifty-five of 88 radiologists (63%) believed that overall error rates did not exceed 10%, and 67 of 88 radiologists (76%) believed that their own individual error rates did not exceed 10%.
CONCLUSIONS: More than 20% of our reports contained potentially confusing errors, and most radiologists believed that report error rates were much lower than they actually were. Knowledge of the frequency and spectrum of errors should raise awareness of this issue and facilitate methods for report improvement.

Entities:  

Mesh:

Year:  2008        PMID: 19027683     DOI: 10.1016/j.jacr.2008.07.005

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  18 in total

1.  Prepopulated radiology report templates: a prospective analysis of error rate and turnaround time.

Authors:  C M Hawkins; S Hall; J Hardin; S Salisbury; A J Towbin
Journal:  J Digit Imaging       Date:  2012-08       Impact factor: 4.056

2.  Non-clinical errors using voice recognition dictation software for radiology reports: a retrospective audit.

Authors:  Chian A Chang; Rodney Strahan; Damien Jolley
Journal:  J Digit Imaging       Date:  2011-08       Impact factor: 4.056

3.  Immediate and sustained benefits of a "total" implementation of speech recognition reporting.

Authors:  J L Hart; A McBride; D Blunt; P Gishen; N Strickland
Journal:  Br J Radiol       Date:  2010-03-11       Impact factor: 3.039

4.  Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors.

Authors:  Matthew J Minn; Arash R Zandieh; Ross W Filice
Journal:  J Digit Imaging       Date:  2015-08       Impact factor: 4.056

5.  Frequency and analysis of non-clinical errors made in radiology reports using the National Integrated Medical Imaging System voice recognition dictation software.

Authors:  R E Motyer; S Liddy; W C Torreggiani; O Buckley
Journal:  Ir J Med Sci       Date:  2016-10-01       Impact factor: 1.568

6.  Asynchronous Speech Recognition Affects Physician Editing of Notes.

Authors:  Kevin J Lybarger; Mari Ostendorf; Eve Riskin; Thomas H Payne; Andrew A White; Meliha Yetisgen
Journal:  Appl Clin Inform       Date:  2018-10-17       Impact factor: 2.342

Review 7.  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

8.  Learning from incident reports in the Australian medical imaging setting: handover and communication errors.

Authors:  N Hannaford; C Mandel; C Crock; K Buckley; F Magrabi; M Ong; S Allen; T Schultz
Journal:  Br J Radiol       Date:  2013-02       Impact factor: 3.039

Review 9.  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

10.  Speech recognition software and electronic psychiatric progress notes: physicians' ratings and preferences.

Authors:  Yaron D Derman; Tamara Arenovich; John Strauss
Journal:  BMC Med Inform Decis Mak       Date:  2010-08-25       Impact factor: 2.796

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