Improving Patient Transfer Protocols for Regional Stroke Networks
Bibliographic record
Abstract
Currently, stroke patients are transported to the nearest stroke center, following specific protocols. Yet, these protocols do not consider many factors, including the spatial variation in population density, the stroke’s severity, the time since stroke onset, and the congestion level at the receiving stroke center. We develop an analytical framework that enriches the stroke transport decision-making process by incorporating these factors. Our research contributes to the literature of stroke care systems by (i) developing the first analytical framework to determine the optimal primary hospital destination in a regional stroke network and (ii) comparing the impact of incorporating prehospital triaging on health outcomes. To this end, we develop an efficient reformulation for allocation problems with stochastic demand and multiserver system under congestion. We derive data-driven outcome prediction models embedded in mixed integer second-order cone programming formulation. Our framework is applied to two real-life cases: Montreal and Quebec City Stroke Networks. We show that adopting a triage strategy could lead to significantly improved health outcomes, where the magnitude of these improvements varies with the networks’ sizes and congestion levels. In the Montreal case, our proposed policy may increase the ratio of patients for therapeutic intervention eligibility by 12.5% while improving by 69% the number of patients with more than two days of emergency department boarding delays. Our results reveal that it is important to consider the network’s characteristics in making a decision for or against implementing a prehospital triage strategy. Finally, we propose a heuristic policy that provides a promising performance while also being easy to implement. This paper was accepted by Stefan Scholtes, healthcare 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".