Literature DB >> 856753

Improving diagnostic accuracy: a comparison of interactive and Delphi consultations.

B J Hillman, S J Hessel, R G Swensson, P G Herman.   

Abstract

Consultation among physicians on difficult diagnostic problems is commonly used to improve the accuracy of medical decisions. Such consultation is most often informal and interactive. Nevertheless, behavioral studies suggest that non-interactive techniques may be more effective problem solving methods. Of these the Delphi approach, involving pooling and feedback of anonymously contributed information, has generated particular interest. To assess the relative effectiveness of independent decision making, interactive group consultation, and Delphi techniques in a clinical setting we compared the diagnostic accuracy of 17 radiologists interpreting radiologic examinations in these settings. Interactive consultation improved performance by 69% compared to radiologists interpreting the studies individually. In addition, two Delphi strategies each produced an additional 20% mean improvement in accuracy over interactive consultation. Whereas interactive consultation improved the accuracy of the best individual readers by only 6%, a Delphi model improved their performance by 25%. Thus, Delphi was an effective, easily applied method of clinical consultation whose usefulness in other clinical setting should be evaluated.

Mesh:

Year:  1977        PMID: 856753     DOI: 10.1097/00004424-197703000-00002

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  5 in total

1.  Automated retrieval of CT images of liver lesions on the basis of image similarity: method and preliminary results.

Authors:  Sandy A Napel; Christopher F Beaulieu; Cesar Rodriguez; Jingyu Cui; Jiajing Xu; Ankit Gupta; Daniel Korenblum; Hayit Greenspan; Yongjun Ma; Daniel L Rubin
Journal:  Radiology       Date:  2010-05-26       Impact factor: 11.105

2.  Special Section Guest Editorial:Radiomics and Imaging Genomics: Quantitative Imaging for Precision Medicine.

Authors:  Sandy Napel; Maryellen Giger
Journal:  J Med Imaging (Bellingham)       Date:  2015-12-11

3.  The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation.

Authors:  Michael F McNitt-Gray; Samuel G Armato; Charles R Meyer; Anthony P Reeves; Geoffrey McLennan; Richie C Pais; John Freymann; Matthew S Brown; Roger M Engelmann; Peyton H Bland; Gary E Laderach; Chris Piker; Junfeng Guo; Zaid Towfic; David P-Y Qing; David F Yankelevitz; Denise R Aberle; Edwin J R van Beek; Heber MacMahon; Ella A Kazerooni; Barbara Y Croft; Laurence P Clarke
Journal:  Acad Radiol       Date:  2007-12       Impact factor: 3.173

4.  The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans.

Authors:  Samuel G Armato; Michael F McNitt-Gray; Anthony P Reeves; Charles R Meyer; Geoffrey McLennan; Denise R Aberle; Ella A Kazerooni; Heber MacMahon; Edwin J R van Beek; David Yankelevitz; Eric A Hoffman; Claudia I Henschke; Rachael Y Roberts; Matthew S Brown; Roger M Engelmann; Richard C Pais; Christopher W Piker; David Qing; Masha Kocherginsky; Barbara Y Croft; Laurence P Clarke
Journal:  Acad Radiol       Date:  2007-11       Impact factor: 3.173

5.  Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval.

Authors:  Jiajing Xu; Sandy Napel; Hayit Greenspan; Christopher F Beaulieu; Neeraj Agrawal; Daniel Rubin
Journal:  Med Phys       Date:  2012-09       Impact factor: 4.071

  5 in total

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