| Title: |
Reducing Inappropriate Abdominal X-ray Requests in a Surgical Admissions Unit: A Quality Improvement Project. |
| Authors: |
Barrington, Michael; Ragatha, Pranu; O'Malley, Emily-Jane; Bethune, Rob |
| Source: |
Cureus: Journal of Medical Science; Sep2025, Vol. 17 Issue 9, p1-8, 8p |
| Subject Terms: |
RADIOLOGY; COMPUTED tomography; REFORMS; MEDICAL care; COMPUTER-assisted image analysis (Medicine); ABDOMINAL pain; TOTAL quality management |
| Abstract: |
Introduction: Abdominal X-rays (AXRs) are a common investigation for acute abdominal pain. Computed tomography (CT) imaging has largely replaced AXRs as the recommended investigation of acute abdominal pain due to the greater detail and improved diagnostic information it can provide. This project aimed to reduce the number of inappropriate AXR imaging requests in a surgical admission unit (SAU). Methods: AXR requests were compared against national radiology guidelines (iRefer) to assess if they were inappropriate. Initial baseline data were collected over three months. Three interventions were introduced to improve the rate of inappropriate requests made: (1) developing locally agreed guidelines for AXR requests, (2) placing guideline posters in the surgical assessment unit, and (3) altering the electronic AXR order form. Data were collected after each intervention period. The number of CT scan requests was collected before and after intervention 3 to assess for concurrent change in volume. Results: At baseline, 69% of AXR requests were considered inappropriate; the majority of requests made were for bowel obstruction (BO, 60%). Overall rates of inappropriate requests improved to 50% after intervention 1, 41% after intervention 2, and 11% after intervention 3. CT scan requests remained similar pre- and post intervention 3. Conclusion: The project has reduced the inappropriate use of AXRs following the implementation of three interventions. [ABSTRACT FROM AUTHOR] |
| : |
Copyright of Cureus: Journal of Medical Science is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |