Automated Structured Reporting for Thyroid Ultrasound: Effect on Reporting Errors and Efficiency.
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2020-08-17
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PURPOSE:To compare the effectiveness of different reporting templates using the ACR Thyroid Imaging Reporting and Data System (TI-RADS) for thyroid ultrasound. METHODS:In this retrospective study, four radiologists implemented ACR TI-RADS while dictating 20 thyroid ultrasounds for each of four different templates: free text, minimally structured, fully structured, fully structured and automated (embedded software automatically sums TI-RADS points, correlates with nodule size, and inserts appropriate recommendation into report impression). In total, 80 reports were constructed per template type. Frequencies of different errors related to ACR TI-RADS were recorded: errors in point assignment, point addition, risk-level assignment, and recommendation. Reporting times were recorded, and a survey about using the template was administered. Differences in error rates were compared using χ2 and Fisher's exact tests, and differences in reporting times were compared using Kruskal-Wallis tests. RESULTS:Across all readers, errors were identified in 27.5% of reports (22 of 80) for the free text template, 28.8% (23 of 80) for the minimally structured template, 18.8% (15 of 80) for the fully structured template, and 0% (0 of 80) for the fully structured and automated template (P < .0001). Frequency of each error type (number assignment, addition, TR categorization, recommendation) decreased across the four templates (P < .0005 to P < .005). Median reporting times for the less complex templates were 210 to 240 seconds, whereas the median automated template reporting time was 180 seconds (P = .41). Radiologists subjectively preferred using the automated template. CONCLUSION:A structured reporting template for thyroid ultrasound that automatically executed steps of ACR TI-RADS resulted in fewer reporting errors for radiologists.
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Wildman-Tobriner, Benjamin, Lawrence Ngo, Tracy A Jaffe, Wendy L Ehieli, Lisa M Ho, Reginald Lerebours, Sheng Luo, Brian C Allen, et al. (2020). Automated Structured Reporting for Thyroid Ultrasound: Effect on Reporting Errors and Efficiency. Journal of the American College of Radiology : JACR. 10.1016/j.jacr.2020.07.024 Retrieved from https://hdl.handle.net/10161/21383.
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Scholars@Duke
Benjamin Wildman-Tobriner
Tracy Anne Jaffe
Reginald (Gino) Lerebours
Education: Masters Degree, Biostatistics. Harvard University. 2017
Bachelors Degree, Statistics. North Carolina State University. 2015
Overview: Gino currently collaborates with researchers, residents, and clinicians in the Departments of Surgery, Radiology and Infectious Diseases. His main research interests and experience are in statistical programming, data management, statistical modeling, statistical consulting and statistical education.
Sheng Luo
Brian C Allen
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