Impact of 24/7/365 Attending Radiologist Coverage on the Turnaround Time in an Emergency and Trauma Radiology Department
Bibliographic record
Abstract
OBJECTIVE: To study the impact of 24/7/365 attending radiologist coverage on the turnaround time (TAT) of trauma and nontrauma cases in an emergency and trauma radiology department. PATIENTS AND METHODS: This was a retrospective chart review in which TAT of patients coming to the emergency department between 2 periods: (1) December 1, 2012, to September 30, 2013, and (2) January 1, 2017, to January 30, 2018, and whose reports were read by an attending emergency and trauma radiologist was noted. RESULTS: The 24/7/365 radiology coverage was associated with a significant reduction in TAT of computed tomography reports, and the time reduction was comparable between trauma and nontrauma cases. In adjusted models, the extension of radiology coverage was associated with an average of 7.83 hours reduction in overall TAT (95% confidence interval [CI]: 7.44-8.22) for reports related to trauma, in which 2.73 hours were due to reduction in completion to transcription time (TC; 95% CI: 2.53-2.93), and 5.10 hours were due to reduction in transcription to finalization time (TF; 95% CI: 4.75-5.44). For reports related to nontrauma cases, 24/7/365 coverage was associated with an average of 6.07 hours reduction in overall TAT (95% CI: 3.54-8.59), 2.91 hours reduction in TC (95% CI: 1.55-4.26), and 3.16 hours reduction in TF (95% CI: 0.90-5.42). CONCLUSION: Our pilot study demonstrates that the implementation of on-site 24/7/365 attending emergency radiology coverage at a tertiary care center was associated with a reduced TAT for trauma and nontrauma patients imaging studies. Although the magnitude and precision of estimates were slightly higher for trauma cases as compared to nontrauma cases. Trauma examinations stand to benefit the most from 24/7/365 attending level radiology coverage.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".