Quantifying Improved Outcomes, Cost Savings, and Hospital Volume Changes From Optimized Emergency Stroke Transport
Bibliographic record
Abstract
BACKGROUND: A previously published conditional probability model optimizes prehospital emergency transport protocols for patients with suspected large-vessel occlusion by recommending the transport strategy, drip-and-ship or mothership, that results in a higher probability of an excellent outcome. In this study, we create generalized models to quantify the change in annual hospital patient volume, the expected annual increase in the number of patients with an excellent outcome, and the annual cost savings to a single-payer healthcare system resulting from these optimized transport protocols. METHODS: We calculated the expected number of patients with suspected large-vessel occlusion transported by ambulance over a 1-year period in a region of interest, using the annual stroke incidence rate and a large-vessel occlusion screening tool. Assuming transport to the closest hospital is the baseline transport policy across the region (drip-and-ship), we determined the change in annual hospital patient volume from implementing optimized transport protocols. We also calculated the resulting annual increase in the number of patients with an excellent outcome (modified Rankin Score of 0-1 at 90 days) and associated cost savings to a single-payer healthcare system. We then performed a case study applying these generalized models to the stroke system serving the Greater Vancouver and Fraser Valley Area, BC, Canada. RESULTS: In the Greater Vancouver and Fraser Valley Area, there was an annual increase of 36 patients with an excellent outcome, translating to an annual cost savings of CA$2 182 824 to the British Columbia healthcare system. We also studied how these results change depending on our assumptions of treatment times at the regional stroke centers. CONCLUSIONS: Our framework quantifies the impact of optimized emergency stroke transport protocols on hospital volume, outcomes, and cost savings to a single-payer healthcare system. When applied to a specific region of interest, these models can help inform health policies concerning emergency transport of patients with suspected large-vessel occlusion.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".