Clinical impact of extending after-hours radiology coverage for emergency department computed tomography imaging
Bibliographic record
Abstract
Samer Dabbo,1 Catherine Varner,2,3 Robert Bleakney,1,2 Howard Ovens2,31University Health Network, Department of Medical Imaging, University of Toronto, 2Division of Emergency Medicine, Schwartz/Reisman Emergency Medicine Institute, Mount Sinai Hospital, 3Department of Family and Community Medicine, University of Toronto, Toronto, CanadaBackground: Academic emergency departments (EDs) are often reliant on preliminary interpretation by radiology residents for after-hours computed tomography (CT) images. Identifying residents’ errors in diagnostic interpretation and ensuring appropriate contact with affected patients are areas of continuing concern.Objective: The Mount Sinai Hospital ED and Medical Imaging Department in Toronto, Canada sought to examine the clinical impact of extending reporting hours of senior attending radiologists for ED patients undergoing CT imaging.Methods: All evening CT studies were read by the on-call sub-specialist staff radiologist before 10 pm; while studies done after 10 pm were read by 8 am, permitting review of final reports by the ordering ED physician. A retrospective review of radiology and ED metrics was performed on ED patients undergoing CT imaging 12 weeks before and 12 weeks after implementation of the extended reading hours.Results: In the 12 weeks prior to implementation of extended senior attending radiologist coverage, 871 CT scans were performed as compared to 944 CT scans after implementation. Time from performance of CT scan to obtaining a dictated report decreased from 10.4 hours to 2.8 hours (P<0.001), and time from performance of CT scan to report verification by the radiologist decreased from 29.7 hours to 9.4 hours (P<0.001). There were no statistically significant changes in ED length of stay, rates of admission, or rates of consultation. However, there was a significant reduction in (median) time taken for ED physicians to resolve discrepant reports in the radiology information system queue (20.7 hours versus 13.3 hours, P<0.001).Conclusion: The extension of reporting hours reduced the time for ED physicians to review discrepant reports, while balancing educational needs of residents. This project has been considered a success by stakeholders and has now been implemented on a permanent basis.Keywords: quality assurance, clinical competence, diagnostic errors, interns, residents
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".