Emergency Medical Service System Design under Service Level Constraints for Heterogeneous Patients
Bibliographic record
Abstract
We study the problem of locating Emergency Medical Service (EMS) facilities in the presence of service level constraints for patients with acuity levels ranging from resuscitation to non-urgent. Each patient arriving at any EMS facility is triaged as either resuscitation/high priority or less urgent/low priority, where high priority patients are always served on a priority basis. The problem is to optimally locate EMS facilities and allocate their service zones to satisfy the following coverage and service level constraints: (i) each user zone is served by an EMS facility that is within a given coverage radius; (ii) at least h proportion of the resuscitation cases at any EMS facility should be admitted immediately without having to wait; (iii) at least l proportion of the cases belonging to low priority class at any EMS facility should not have to wait for more than l minutes. For this, we model the network of EMS facilities as spatially distributed M/M/1 priority queues, whose locations and user allocations need to be determined. The resulting integer programming problem is challenging to solve, especially in absence of any known analytical expression for the waiting time distribution of low priority customers in an M/M/1 priority queue. We develop a cutting plane based solution algorithm, exploiting the concavity of the waiting time distribution of low priority customers to approximate its non-linearity using tangent planes, determined numerically using matrix geometric method. Using a case study of locating EMS facilities in Austin, Texas, we present computational results and managerial insights.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".