Rate of Inappropriate Imaging Utilization by the Emergency Department in Community Hospitals
Bibliographic record
Abstract
Objective: To retrospectively analyse the use of imaging studies in the Emergency Department of community hospitals using evidence based guidelines and clinical judgement. Methods: Medical records of 661 patients who visited the Emergency Department (ED) in 2015 and underwent imaging studies were reviewed. The Canadian Association of Radiologists, American College of Radiologists and Choosing Wisely Canada guidelines were used to determine the appropriateness of imaging studies. The use of prior patient imaging, the rate at which studies were repeated and the respective impacts on patient management of the imaging studies were also examined. Results: Of the 1056 imaging studies reviewed, 228 (22%) were found to be clinical situations where no imaging study was indicated while 168 (16%) were considered a suboptimal choice of imaging study or modality. When no study was recommended, a positive impact on the diagnosis was noted in 105 (46%) cases and on patient management 83 (36%) times. Notably, 219 (21%) patients had a relevant examination performed in the last 30 days, and 147 (14%) reports noted that the results of the prior study also concurred with the imaging study evaluated. Conclusion: In this study, 228 (22%) radiographs and CT studies, excluding MVC related imaging and extremity imaging, were not indicated based on appropriateness criteria and consequently had a limited impact on patient management. This supports the need for increased clinical decision support for ED physicians, regional health information exchanges and consideration of Computerized Physician Order Entry in the ED with embedded appropriateness criteria at the point of ordering.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".