Impact of 24/7 Onsite Emergency Radiology Staff Coverage on Emergency Department Workflow
Bibliographic record
Abstract
PURPOSE: Assess the impact of 24/7/365 emergency trauma radiology (ETR) coverage on Emergency Department (ED) patient flow in an urban, quaternary-care teaching hospital. METHODS: Patient ED visit and imaging information were extracted from the hospital patient care information system for 2008 to 2018. An interrupted time-series approach with a comparison group was used to study the impact of 24/7/365 ETR on average monthly ED length of stay (ED-LOS) and Emergency Physician to disposition time (EP-DISP). Linear regression models were fit with abrupt and permanent interrupts for 24/7/365 ETR, a coefficient for comparison series and a SARIMA error term; subgroup analyses were performed by patient arrival time, imaging type and chief complaint. RESULTS: During the study period, there were 949,029 ED visits and 739,796 diagnostic tests. Following implementation of 24/7/365 coverage, we found a significant decrease in EP-DISP time for patients requiring only radiographs (-29 min;95%CI:-52,-6) and a significant increase in EP-DISP time for major trauma patients (46 min;95%CI:13,79). No significant change in patient throughput was observed during evening hours for any patient subgroup. For overnight patients, there was a reduction in EP-DISP for patients with symptoms consistent with stroke (-78 min;95%CI:-131,-24) and for high acuity patients who required imaging (-33 min;95%CI:-57,-10). Changes in ED-LOS followed a similar pattern. CONCLUSIONS: At our institution, 24/7/365 in-house ETR staff radiology coverage was associated with improved ED flow for patients requiring only radiographs and for overnight stroke and high acuity patients. Major trauma patients spent more time in the ED, perhaps reflecting the required multidisciplinary management.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".