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Proceedings Paper

Applying a social network analysis (SNA) approach to understanding radiologists' performance in reading mammograms
Author(s): Seyedamir Tavakoli Taba; Liaquat Hossain; Robert Heard; Patrick Brennan; Warwick Lee; Sarah Lewis
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Paper Abstract

Rationale and objectives: Observer performance has been widely studied through examining the characteristics of individuals. Applying a systems perspective, while understanding of the system’s output, requires a study of the interactions between observers. This research explains a mixed methods approach to applying a social network analysis (SNA), together with a more traditional approach of examining personal/ individual characteristics in understanding observer performance in mammography. Materials and Methods: Using social networks theories and measures in order to understand observer performance, we designed a social networks survey instrument for collecting personal and network data about observers involved in mammography performance studies. We present the results of a study by our group where 31 Australian breast radiologists originally reviewed 60 mammographic cases (comprising of 20 abnormal and 40 normal cases) and then completed an online questionnaire about their social networks and personal characteristics. A jackknife free response operating characteristic (JAFROC) method was used to measure performance of radiologists. JAFROC was tested against various personal and network measures to verify the theoretical model. Results: The results from this study suggest a strong association between social networks and observer performance for Australian radiologists. Network factors accounted for 48% of variance in observer performance, in comparison to 15.5% for the personal characteristics for this study group. Conclusion: This study suggest a strong new direction for research into improving observer performance. Future studies in observer performance should consider social networks’ influence as part of their research paradigm, with equal or greater vigour than traditional constructs of personal characteristics.

Paper Details

Date Published: 10 March 2017
PDF: 7 pages
Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101360F (10 March 2017); doi: 10.1117/12.2254177
Show Author Affiliations
Seyedamir Tavakoli Taba, Faculty of Health Sciences, The Univ. of Sydney (Australia)
Liaquat Hossain, The Univ. of Hong Kong (Hong Kong, China)
Robert Heard, Faculty of Health Sciences, The Univ. of Sydney (Australia)
Patrick Brennan, Faculty of Health Sciences, The Univ. of Sydney (Australia)
Warwick Lee, Faculty of Health Sciences, The Univ. of Sydney (Australia)
Sarah Lewis, Faculty of Health Sciences, The Univ. of Sydney (Australia)


Published in SPIE Proceedings Vol. 10136:
Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment
Matthew A. Kupinski; Robert M. Nishikawa, Editor(s)

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