Automated Structured Reporting for Thyroid Ultrasound: Effect on Reporting Errors and Efficiency.

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.

Duke Scholars

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Author Tracy Anne Jaffe Radiology, Abdominal Imaging

Author Lisa Mei-ling Ho Radiology, Abdominal Imaging

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Author Sheng Luo Biostatistics & Bioinformatics, Division of Biostatistics

Author Brian C Allen Radiology, Abdominal Imaging