A Systematic Review of Interventions to Reduce Computed Tomography Usage in the Emergency Department
Bibliographic record
Abstract
STUDY OBJECTIVE: Unnecessary computed tomography (CT) scans burden the health care system, leading to increased emergency department (ED) wait times and lengths of stay, costing almost a billion dollars annually. This study aimed to describe ED-based interventions that are most effective at reducing CT imaging while maintaining diagnostic accuracy and patient safety. METHODS: Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, MEDLINE, Embase, CINAHL, Cochrane Central Register of Controlled Trials, and Google Scholar were searched until December 31, 2020. Randomized and nonrandomized studies that assessed the effect of an ED-based intervention on CT scan usage were included. Abstract screening, data extraction, and quality assessment were conducted in duplicate. The Grading of Recommendation Assessment, Development and Evaluation framework, with the Risk of Bias 2 and Risk of Bias in Nonrandomized Studies - of Interventions tools, was used to determine the certainty of evidence. Significant clinical and statistical heterogeneity precluded meta-analysis; hence, a narrative synthesis was conducted. RESULTS: A total of 149 studies were included of 5,667 screened abstracts, with substantial interrater reliability among reviewers (Cohen's κ>0.60). The CT reduction strategies were categorized into 15 single and 11 multimodal interventions by consensus review. Interventions that consistently reduced CT usage included diagnostic pathways, alternative test availability, specialist involvement, and provider feedback. Family/patient education, clinical decision support tools, or passive guideline dissemination did not consistently reduce usage. Only 44% of studies reported unintended consequences of reduction strategies; however, these showed no increase in missed diagnoses or patient harm. The interventions that engaged multiple specialties during planning/implementation had a greater reduction effect than ED only. The certainty of evidence for the primary outcome was very low. CONCLUSION: Multidisciplinary-led interventions that provided an alternative to CT imaging were the most effective at reducing usage and did so without compromising patient safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".