Abstract
Purpose: Universities need to deliver educational programmes that create radiography graduates who are ready and able to participate in abnormality detection schemes, ultimately delivering safe and reliable performance because junior doctors are exposed to the risk of misdiagnosis if unsupported by other healthcare professionals. Radiographers are ideally suited to this role having the responsibility for conducting the actual X-ray examination. Method: The image interpretation performance of one cohort of student radiographers was measured upon enrolment from UCAS in the first week of university education and then again prior to graduation using RadBench (n ¼ 23). Results: The results identified that novices have a range of natural image interpretation skills; accuracy 35e85%, sensitivity 45e100%, specificity 15e85%, mean ROC 0.691. Graduates presented a narrower range; accuracy 60e90%, sensitivity 40e100%, specificity 60e90%, mean ROC 0.841. The positive shift in graduate mean accuracy (þ16%) was driven by increases in specificity (þ27%) rather than sensitivity (þ5%). No statistically significant differences (ANOVA) could be found between age group, gender and previous education however trends were identified. 56.5% of the population (n ¼ 13) met a benchmark accurate standard of 80%, including one graduate who met 90%. Conclusion: Image interpretation testing at the point of UCAS entry is a useful indicator of future performance and is a recommended factor for consideration as part of the selection process. Whilst image interpretation now forms an integral part of undergraduate radiography programmes, new graduates may not necessary possess the reliability in decision making to justify participation in abnormality detection schemes, highlighting the need for continuous professional development.
Original language | English |
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Pages (from-to) | e1-e7 |
Journal | Radiography |
DOIs | |
Publication status | Published - 31 Aug 2016 |
Externally published | Yes |
Keywords
- Red Dot
- PCE
- Radiographer
- Clinical Governance
- RadBench
- Image Interpretation