Literature DB >> 22270786

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

C M Hawkins1, S Hall, J Hardin, S Salisbury, A J Towbin.   

Abstract

Current speech recognition software allows exam-specific standard reports to be prepopulated into the dictation field based on the radiology information system procedure code. While it is thought that prepopulating reports can decrease the time required to dictate a study and the overall number of errors in the final report, this hypothesis has not been studied in a clinical setting. A prospective study was performed. During the first week, radiologists dictated all studies using prepopulated standard reports. During the second week, all studies were dictated after prepopulated reports had been disabled. Final radiology reports were evaluated for 11 different types of errors. Each error within a report was classified individually. The median time required to dictate an exam was compared between the 2 weeks. There were 12,387 reports dictated during the study, of which, 1,173 randomly distributed reports were analyzed for errors. There was no difference in the number of errors per report between the 2 weeks; however, radiologists overwhelmingly preferred using a standard report both weeks. Grammatical errors were by far the most common error type, followed by missense errors and errors of omission. There was no significant difference in the median dictation time when comparing studies performed each week. The use of prepopulated reports does not alone affect the error rate or dictation time of radiology reports. While it is a useful feature for radiologists, it must be coupled with other strategies in order to decrease errors.

Mesh:

Year:  2012        PMID: 22270786      PMCID: PMC3389086          DOI: 10.1007/s10278-012-9455-9

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  9 in total

1.  Evaluation of the accuracy of a continuous speech recognition software system in radiology.

Authors:  K M Kanal; N J Hangiandreou; A M Sykes; H E Eklund; P A Araoz; J A Leon; B J Erickson
Journal:  J Digit Imaging       Date:  2000-05       Impact factor: 4.056

2.  Voice recognition software: effect on radiology report turnaround time at an academic medical center.

Authors:  Arun Krishnaraj; Joseph K T Lee; Sandra A Laws; T Jay Crawford
Journal:  AJR Am J Roentgenol       Date:  2010-07       Impact factor: 3.959

3.  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

4.  Economics of radiology report editing using voice recognition technology.

Authors:  William R Reinus
Journal:  J Am Coll Radiol       Date:  2007-12       Impact factor: 5.532

Review 5.  Radiology reporting, past, present, and future: the radiologist's perspective.

Authors:  Bruce I Reiner; Nancy Knight; Eliot L Siegel
Journal:  J Am Coll Radiol       Date:  2007-05       Impact factor: 5.532

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

Authors:  Leslie E Quint; Douglas J Quint; James D Myles
Journal:  J Am Coll Radiol       Date:  2008-12       Impact factor: 5.532

7.  Effect of voice recognition on radiologist reporting time.

Authors:  Sasha N Bhan; Craig L Coblentz; Geoffrey R Norman; Sammy H Ali
Journal:  Can Assoc Radiol J       Date:  2008-10       Impact factor: 2.248

8.  The effect of voice recognition software on comparative error rates in radiology reports.

Authors:  S McGurk; K Brauer; T V Macfarlane; K A Duncan
Journal:  Br J Radiol       Date:  2008-07-15       Impact factor: 3.039

9.  Improvement of report workflow and productivity using speech recognition--a follow-up study.

Authors:  Tomi Kauppinen; Mika P Koivikko; Juhani Ahovuo
Journal:  J Digit Imaging       Date:  2008-04-24       Impact factor: 4.056

  9 in total
  14 in total

1.  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

Review 2.  The state of structured reporting: the nuance of standardized language.

Authors:  Lindsey A G Shea; Alexander J Towbin
Journal:  Pediatr Radiol       Date:  2019-03-29

3.  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

Review 4.  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

Review 5.  [Reporting initiatives. An update on treatment in radiology].

Authors:  J-M Hempel; D Pinto dos Santos; R Kloeckner; C Dueber; P Mildenberger
Journal:  Radiologe       Date:  2014-07       Impact factor: 0.635

6.  Creation and implementation of department-wide structured reports: an analysis of the impact on error rate in radiology reports.

Authors:  C Matthew Hawkins; Seth Hall; Bin Zhang; Alexander J Towbin
Journal:  J Digit Imaging       Date:  2014-10       Impact factor: 4.056

7.  Structured report compliance: effect on audio dictation time, report length, and total radiologist study time.

Authors:  Tarek N Hanna; Haris Shekhani; Kiran Maddu; Chao Zhang; Zhengjia Chen; Jamlik-Omari Johnson
Journal:  Emerg Radiol       Date:  2016-06-25

8.  Structured reporting of MRI of the shoulder - improvement of report quality?

Authors:  Sebastian Gassenmaier; Marco Armbruster; Florian Haasters; Tobias Helfen; Thomas Henzler; Sedat Alibek; Dominik Pförringer; Wieland H Sommer; Nora N Sommer
Journal:  Eur Radiol       Date:  2017-03-13       Impact factor: 5.315

9.  A default normal chest CT structured reporting field for coronary calcifications does not cause excessive false-negative reporting.

Authors:  William R Walter; Shlomit Goldberg-Stein; Jeffrey M Levsky; Hillel W Cohen; Meir H Scheinfeld
Journal:  J Am Coll Radiol       Date:  2015-05-16       Impact factor: 5.532

10.  Structured reporting in petrous bone MRI examinations: impact on report completeness and quality.

Authors:  Marco Armbruster; Sebastian Gassenmaier; Mareike Haack; Maximilian Reiter; Dominik Nörenberg; Thomas Henzler; Nora N Sommer; Wieland H Sommer; Franziska Braun
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-07-23       Impact factor: 2.924

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