ArticleInsights into imaging2023
Does clinical decision support system promote expert consensus for appropriate imaging referrals? Chest-abdominal-pelvis CT as a case study.
Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Sex differences in inappropriate imaging requests: insights from the Medical Imaging Decision And Support (MIDAS) study.European radiology · 2026Trial
- Justification of Imaging Referrals: A Cross-Sectional Survey of Referring Physicians' Knowledge, Practices, and Barriers.AJPM focus · 2026Article
- Decreasing inappropriate MRCP with contrast exams: impact of an EMR-Embedded clinical care pathway.Abdominal radiology (New York) · 2025Article
- Comparison of CT referral justification using clinical decision support and large language models in a large European cohort.European radiology · 2025Article
- Challenges and insights from implementing clinical decision support systems for radiologic imaging: experience from the MIDAS trial.Insights into imaging · 2025Review
- Computed tomography referral guidelines adherence in Europe: insights from a seven-country audit.European radiology · 2025Article
- Evaluating the Accuracy and Impact of the ESR-iGuide Decision Support Tool in Optimizing CT Imaging Referral Appropriateness.Journal of imaging informatics in medicine · 2025Article
- Radiologic Decision-Making for Imaging in Pulmonary Embolism: Accuracy and Reliability of Large Language Models-Bing, Claude, ChatGPT, and Perplexity.The Indian journal of radiology & imaging · 2024Article
- Insights from a web-based questionnaire: examining diagnostic procedures prior to magnetic resonance imaging.Israel journal of health policy research · 2024Article
- A content-aware chatbot based on GPT 4 provides trustworthy recommendations for Cone-Beam CT guidelines in dental imaging.Dento maxillo facial radiology · 2024Article
- Inappropriate CT examinations: how much, who and where? Insights from a clinical decision support system (CDSS) analysis.European radiology · 2023Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe assessed the appropriateness of chest-abdominal-pelvis (CAP) CT scan use in the Emergency Department (ED), based on expert physicians and the ESR iGuide, a clinical decision support system (CDSS).
methodsA retrospective cross-study was conducted. We included 100 cases of CAP-CT scans ordered at the ED. Four experts rated the appropriateness of the cases on a 7-point scale, before and after using the decision support tool.
resultsBefore using the ESR iGuide the overall mean rating of the experts was 5.2 ± 1.066, and it increased slightly after using the system (5.85 ± 0.911 (p < 0.01)). Using a threshold of 5 (on a 7-level scale), the experts considered only 63% of the tests appropriate before using the ESR iGuide. The number increased to 89% after consultation with the system. The degree of overall agreement among the experts was 0.388 before ESR iGuide consultation and 0.572 after consultation. According to the ESR iGuide, for 85% of the cases, CAP CT was not a recommended option (score 0). Abdominal-Pelvis CT was "usually appropriate" for 65 out of the 85 (76%) cases (score 7-9). 9% of the cases did not require CT as first exam modality.
conclusionsAccording to both the experts and the ESR iGuide, inappropriate testing was prevalent, in terms of both frequency of the scans and also inappropriately chosen body regions. These findings raise the need for unified workflows that might be achieved using a CDSS. Further studies are needed to investigate the CDSS contribution to the informed decision-making and increased uniformity among different expert physicians when ordering the appropriate test.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.