Implementing and evaluating the efficacy of an acute care urology model of care in a large community hospital
Bibliographic record
Abstract
INTRODUCTION: We implemented an acute care urology (ACU) model at a large Canadian community hospital to determine the impacts on safe and timely care of patients with renal colic. The model includes a dedicated ACU surgeon, a clinic for emergency department (ED) referrals, and additional daytime operating room (OR) blocks for urgent cases. METHODS: We conducted a chart review of 579 patients presenting to the ED with renal colic. Data was collected before (pre-intervention, September to November 2015) and after (post-intervention, September to November 2016) implementation of the ACU model. Secondary methods of evaluation included surveying patients and 20 ED physicians to capture subjective feedback. RESULTS: Of the 579 patients presenting with renal colic,194 were diagnosed with an obstructing kidney stone and were referred to urology for outpatient care. The ED-to-clinic time was significantly lower for those in the ACU model (p<0.001). Furthermore, the ACU clinic resulted in significantly more patients being referred for outpatient care (p=0.0004). There was also higher likelihood that patients would successfully obtain an appointment post-referral (p=0.0242). The number of after-hours and weekend surgeries decreased significantly after dedicated ACU daytime OR blocks were added in September 2015 (p<0.0001). All surveyed patients rated the care as either "excellent" or "very good," and all physicians believed the ACU model has improved patient care. CONCLUSIONS: The ACU model has shown benefit in ensuring timely followup for ED patients, reducing use of after-hour OR time, and improving patient and physician satisfaction.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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".