Literature DB >> 24848854

Educational interventions to improve screening mammography interpretation: a randomized controlled trial.

Berta M Geller1, Andy Bogart, Patricia A Carney, Edward A Sickles, Robert Smith, Barbara Monsees, Lawrence W Bassett, Diana M Buist, Karla Kerlikowske, Tracy Onega, Bonnie C Yankaskas, Sebastien Haneuse, Deirdre Hill, Matthew G Wallis, Diana Miglioretti.   

Abstract

OBJECTIVE: The objective of our study was to conduct a randomized controlled trial of educational interventions that were created to improve performance of screening mammography interpretation.
MATERIALS AND METHODS: We randomly assigned physicians who interpret mammography to one of three groups: self-paced DVD, live expert-led educational seminar, or control. The DVD and seminar interventions used mammography cases of varying difficulty and provided associated teaching points. Interpretive performance was compared using a pretest-posttest design. Sensitivity, specificity, and positive predictive value (PPV) were calculated relative to two outcomes: cancer status and consensus of three experts about recall. The performance measures for each group were compared using logistic regression adjusting for pretest performance.
RESULTS: One hundred two radiologists completed all aspects of the trial. After adjustment for preintervention performance, the odds of improved sensitivity for correctly identifying a lesion relative to expert recall were 1.34 times higher for DVD participants than for control subjects (95% CI, 1.00-1.81; p = 0.050). The odds of an improved PPV for correctly identifying a lesion relative to both expert recall (odds ratio [OR] = 1.94; 95% CI, 1.24-3.05; p = 0.004) and cancer status (OR = 1.81; 95% CI, 1.01-3.23; p = 0.045) were significantly improved for DVD participants compared with control subjects, with no significant change in specificity. For the seminar group, specificity was significantly lower than the control group (OR relative to expert recall = 0.80; 95% CI, 0.64-1.00; p = 0.048; OR relative to cancer status = 0.79; 95% CI, 0.65-0.95; p = 0.015).
CONCLUSION: In this randomized controlled trial, the DVD educational intervention resulted in a significant improvement in screening mammography interpretive performance on a test set, which could translate into improved interpretative performance in clinical practice.

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Year:  2014        PMID: 24848854      PMCID: PMC4276372          DOI: 10.2214/AJR.13.11147

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  18 in total

1.  Accuracy of screening mammography interpretation by characteristics of radiologists.

Authors:  William E Barlow; Chen Chi; Patricia A Carney; Stephen H Taplin; Carl D'Orsi; Gary Cutter; R Edward Hendrick; Joann G Elmore
Journal:  J Natl Cancer Inst       Date:  2004-12-15       Impact factor: 13.506

2.  Does litigation influence medical practice? The influence of community radiologists' medical malpractice perceptions and experience on screening mammography.

Authors:  Joann G Elmore; Stephen H Taplin; William E Barlow; Gary R Cutter; Carl J D'Orsi; R Edward Hendrick; Linn A Abraham; Jessica S Fosse; Patricia A Carney
Journal:  Radiology       Date:  2005-07       Impact factor: 11.105

3.  Improvement in mammography interpretation skills in a community radiology practice after dedicated teaching courses: 2-year medical audit of 38,633 cases.

Authors:  M N Linver; S B Paster; R D Rosenberg; C R Key; C A Stidley; W V King
Journal:  Radiology       Date:  1992-07       Impact factor: 11.105

4.  Breast screening: PERFORMS identifies key mammographic training needs.

Authors:  H J Scott; A G Gale
Journal:  Br J Radiol       Date:  2006-12       Impact factor: 3.039

5.  A portrait of breast imaging specialists and of the interpretation of mammography in the United States.

Authors:  Rebecca S Lewis; Jonathan H Sunshine; Mythreyi Bhargavan
Journal:  AJR Am J Roentgenol       Date:  2006-11       Impact factor: 3.959

6.  Computer-assisted mammography feedback program (CAMFP) an electronic tool for continuing medical education.

Authors:  Nicole Urban; Gary M Longton; Andrea D Crowe; Mariann J Drucker; Constance D Lehman; Susan Peacock; Kimberly A Lowe; Steve B Zeliadt; Marcia A Gaul
Journal:  Acad Radiol       Date:  2007-09       Impact factor: 3.173

7.  Breast Cancer Surveillance Consortium: a national mammography screening and outcomes database.

Authors:  R Ballard-Barbash; S H Taplin; B C Yankaskas; V L Ernster; R D Rosenberg; P A Carney; W E Barlow; B M Geller; K Kerlikowske; B K Edwards; C F Lynch; N Urban; C A Chrvala; C R Key; S P Poplack; J K Worden; L G Kessler
Journal:  AJR Am J Roentgenol       Date:  1997-10       Impact factor: 3.959

8.  Current medicolegal and confidentiality issues in large, multicenter research programs.

Authors:  P A Carney; B M Geller; H Moffett; M Ganger; M Sewell; W E Barlow; N Stalnaker; S H Taplin; C Sisk; V L Ernster; H A Wilkie; B Yankaskas; S P Poplack; N Urban; M M West; R D Rosenberg; S Michael; T D Mercurio; R Ballard-Barbash
Journal:  Am J Epidemiol       Date:  2000-08-15       Impact factor: 4.897

9.  Physician predictors of mammographic accuracy.

Authors:  Rebecca Smith-Bindman; Philip Chu; Diana L Miglioretti; Chris Quale; Robert D Rosenberg; Gary Cutter; Berta Geller; Peter Bacchetti; Edward A Sickles; Karla Kerlikowske
Journal:  J Natl Cancer Inst       Date:  2005-03-02       Impact factor: 13.506

10.  Establishing a gold standard for test sets: variation in interpretive agreement of expert mammographers.

Authors:  Tracy Onega; Melissa L Anderson; Diana L Miglioretti; Diana S M Buist; Berta Geller; Andy Bogart; Robert A Smith; Edward A Sickles; Barbara Monsees; Lawrence Bassett; Patricia A Carney; Karla Kerlikowske; Bonnie C Yankaskas
Journal:  Acad Radiol       Date:  2013-06       Impact factor: 3.173

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  9 in total

1.  Feasibility and acceptability of conducting a randomized clinical trial designed to improve interpretation of screening mammography.

Authors:  Patricia A Carney; Andy Bogart; Edward A Sickles; Robert Smith; Diana S M Buist; Karla Kerlikowske; Tracy Onega; Diana L Miglioretti; Robert Rosenberg; Bonnie C Yankaskas; Berta M Geller
Journal:  Acad Radiol       Date:  2013-11       Impact factor: 3.173

2.  Effect of radiologists' diagnostic work-up volume on interpretive performance.

Authors:  Diana S M Buist; Melissa L Anderson; Robert A Smith; Patricia A Carney; Diana L Miglioretti; Barbara S Monsees; Edward A Sickles; Stephen H Taplin; Berta M Geller; Bonnie C Yankaskas; Tracy L Onega
Journal:  Radiology       Date:  2014-06-24       Impact factor: 11.105

3.  Boosting medical diagnostics by pooling independent judgments.

Authors:  Ralf H J M Kurvers; Stefan M Herzog; Ralph Hertwig; Jens Krause; Patricia A Carney; Andy Bogart; Giuseppe Argenziano; Iris Zalaudek; Max Wolf
Journal:  Proc Natl Acad Sci U S A       Date:  2016-07-18       Impact factor: 11.205

4.  Correlation Between Screening Mammography Interpretive Performance on a Test Set and Performance in Clinical Practice.

Authors:  Diana L Miglioretti; Laura Ichikawa; Robert A Smith; Diana S M Buist; Patricia A Carney; Berta Geller; Barbara Monsees; Tracy Onega; Robert Rosenberg; Edward A Sickles; Bonnie C Yankaskas; Karla Kerlikowske
Journal:  Acad Radiol       Date:  2017-05-24       Impact factor: 3.173

5.  Experiences of Women Who Refuse Recall for Further Investigation of Abnormal Screening Mammography: A Qualitative Study.

Authors:  Wei-Ying Sung; Hui-Chuan Yang; I-Chen Liao; Yu-Ting Su; Fu-Husan Chen; Shu-Ling Chen
Journal:  Int J Environ Res Public Health       Date:  2022-01-18       Impact factor: 3.390

6.  Pooling decisions decreases variation in response bias and accuracy.

Authors:  Ralf H J M Kurvers; Stefan M Herzog; Ralph Hertwig; Jens Krause; Max Wolf
Journal:  iScience       Date:  2021-06-17

7.  Collective intelligence meets medical decision-making: the collective outperforms the best radiologist.

Authors:  Max Wolf; Jens Krause; Patricia A Carney; Andy Bogart; Ralf H J M Kurvers
Journal:  PLoS One       Date:  2015-08-12       Impact factor: 3.240

8.  Deep learning can be used to train naïve, nonprofessional observers to detect diagnostic visual patterns of certain cancers in mammograms: a proof-of-principle study.

Authors:  Jay Hegdé
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-04

9.  How to detect high-performing individuals and groups: Decision similarity predicts accuracy.

Authors:  R H J M Kurvers; S M Herzog; R Hertwig; J Krause; M Moussaid; G Argenziano; I Zalaudek; P A Carney; M Wolf
Journal:  Sci Adv       Date:  2019-11-20       Impact factor: 14.136

  9 in total

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