General Radiography Image Quality Scorecard

Abstract

Purpose: To describe the Image Quality Audit Framework and highlight the impact of Image Quality Audit.Methods: Radiographers trained in Image Interpretation were recruited to review the radiographic images retrospectively. An Image Quality form was created using “Visual Basic Application” in Microsoft Excel to record the auditors’ findings. There were nine criteria which grade the quality of the images based on technical and clinical aspects. Each criterion consisted of 3-point Likert scale or closed-ended question. The auditors randomly reviewed 5% of the total number of examinations performed by the auditees. The responses were stored, and the programme generated a scorecard automatically. A second auditor was asked to review the images if there is a disagreement between the auditor and auditee in the score achieved.Results: The framework enabled a holistic analysis and accurate identification of radiographer's areas of improvement. Corrective action plans were developed to improve the radiographers’ skill. Continuous active learning was achieved when the auditor and auditee review the results together.Conclusion: A robust clinical audit comprising of both Image Quality and Reject Rate Analysis can improve the quality of images produced. This brings value to the patients by increasing the accuracy of the patient's diagnosis, which allows more targeted treatment. Purpose: To describe the Image Quality Audit Framework and highlight the impact of Image Quality Audit. Methods: Radiographers trained in Image Interpretation were recruited to review the radiographic images retrospectively. An Image Quality form was created using “Visual Basic Application” in Microsoft Excel to record the auditors’ findings. There were nine criteria which grade the quality of the images based on technical and clinical aspects. Each criterion consisted of 3-point Likert scale or closed-ended question. The auditors randomly reviewed 5% of the total number of examinations performed by the auditees. The responses were stored, and the programme generated a scorecard automatically. A second auditor was asked to review the images if there is a disagreement between the auditor and auditee in the score achieved. Results: The framework enabled a holistic analysis and accurate identification of radiographer's areas of improvement. Corrective action plans were developed to improve the radiographers’ skill. Continuous active learning was achieved when the auditor and auditee review the results together. Conclusion: A robust clinical audit comprising of both Image Quality and Reject Rate Analysis can improve the quality of images produced. This brings value to the patients by increasing the accuracy of the patient's diagnosis, which allows more targeted treatment.