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Improving Breast Ultrasound Interpretation in Uganda Using a Condensed Breast Imaging Reporting and Data System
John R Scheel1, Sue Peacock2, Jackson Orem3
1Department of Radiology, Seattle Cancer Care Alliance, University of Washington, 825 Eastlake Avenue East, G2-600, Seattle, WA 98109-1023.
Rationale And Objectives:
This study aimed to determine whether a 2-day educational course using a condensed Breast Imaging Reporting and Data System (condensed BI-RADS) improved the accuracy of Ugandan healthcare workers interpreting breast ultrasound.
Materials And Methods:
The target audience of this intervention was Ugandan healthcare workers involved in performing, interpreting, or acting on the results of breast ultrasound. The educational course consisted of a pretest knowledge assessment, a series of lectures on breast imaging interpretation and standardized reporting using a condensed BI-RADS, and a posttest knowledge assessment. Participants interpreted 53 different ultrasound test cases by selecting the finding type, descriptors for masses, and recommendations. We compared the percent correct on the pretest and posttest based on occupation and training level.
Results:
Sixty-one Ugandan healthcare workers participated in this study, including 13 radiologists, 13 other physicians, 12 technologists, and 23 midlevel providers. Most groups improved in identifying the finding type (P < 0.05). All occupations showed improved use of descriptive terms for the shape and internal echogenicity of masses (P < 0.05). Most groups showed significant improvement in recommendations for normal and benign findings with a corresponding reduction in biopsy recommendations.
Conclusions:
Targeted breast ultrasound education using a condensed BI-RADS improved the interpretive performance of healthcare workers and was particularly successful in reducing the frequency of unnecessary biopsies for normal and benign findings. Multimodal educational efforts to improve accuracy and management of breast ultrasound findings may augment breast cancer early detection efforts in resource-limited settings.
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