RadBench: Benchmarking image interpretation skills. Issue 2 (May 2016)

• Record Type: Journal Article
• Authors: Wright, C.; Reeves, P.
• Is Part Of: Radiography. Volume 22:Issue 2(2016)
• Page Start: e131
• Page End: e136
• Publication Date: 2016-05
• Subjects: Abnormality detection -- Accreditation -- Accuracy -- Audit -- CPD -- Development needs; Diagnostic imaging -- Periodicals; Radiotherapy -- Periodicals; Cancer -- Radiotherapy -- Periodicals; Diagnostic Imaging -- Periodicals; Neoplasms -- Periodicals; Radiotherapy -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Radiothérapie -- Périodiques; Cancer -- Radiothérapie -- Périodiques; Electronic journals; 616.0757
• DOI: 10.1016/j.radi.2015.12.010
• Languages: English
• ISSNs: 1078-8174

Abstract

Abstract: Purpose: The key aim of this research was to develop an objective, accurate assessment tool with which to provide regular measurement and monitoring of image interpretation performance. The tool was a specially developed software program ( RadBench ) by which to objectively measure image interpretation performance en masse and identify development needs. Method: Two test banks were generated (Test 1 & Test 2), each containing twenty appendicular musculoskeletal images, half were normal, half contained fractures. All images were double reported by radiologists and anonymised. A study (n = 42) was carried out within one calendar month to test the method and analysis approach. The participants included general radiographers (34), reporting radiographers (3), radiologists (2) (all from one UK NHS Trust) and medical imaging academics (3). Results: The RadBench software generated calculations of sensitivity, specificity, and accuracy in addition to a decision making map for each respondent. Early findings highlighted a 5% mean difference between image banks, confirming that benchmarking must be related to a specific test. The benchmarking option within the software enabled the user to compare their score with the highest, lowest and mean score of others who had taken the same test. Reporting radiographers and radiologists all scored 95% or above accuracy in both tests. The general radiographer population scored between 60 and 95%. Conclusions: The evidence from this research indicates that the Radbench tool is capable of providing benchmark measures of image interpretation accuracy, with the potential for comparison across populations. Highlights: RadBench software provides the opportunity to assess image interpretation skills en masse and across different populations. RadBench provides an independent objective assessment tool. Identifying image interpretation training needs provides focus and increased return on investment. RadBench has the potential to help underpin the developing role of Radiographers within healthcare.