Search arXivSearch

arXiv · 2505.23560

Ambulance Allocation for Patient-Centered Care

Abstract

Emergency Medical Services (EMS) in the United States and similar systems typically utilize a single treatment pathway, transporting all patients to emergency departments (EDs), regardless of their actual care needs or preferences. Recent policy reforms have sought to introduce alternative treatment pathways to divert lower acuity patients from the ED, but operationalizing these options has proven difficult. This paper proposes a patient-centered EMS (PC-EMS) ambulance allocation model that supports multiple care pathways by aligning EMS responses with individual patient needs. We develop a two-stage mixed-integer optimization framework that incorporates multiple dispatch and secondary assignment strategies which enable dynamic resource deployment. The model maximizes appropriate ED diversions while maintaining ambulance availability using a queueing-based availability constraint. We leverage national EMS data and machine learning to estimate dispatcher accuracy and diversion potential. Simulations across diverse geographic regions suggest that agencies can achieve up to 80% of possible ED diversions by equipping only 15 to 25% of their fleet with diversion capable units. Adaptive dispatch strategies improve diversion rates by 3.4 to 8.6 times compared to conventional single unit dispatch. These results provide actionable guidance for PC-EMS implementation by quantifying the trade off between equipment investment and operational coordination. Using the allocation model, agencies can strategically choose between upgrading fewer units with advanced dispatching protocols versus larger fleet investments with simpler operations. This approach offers flexible pathways suited to different organizational capabilities and implementation readiness.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Eric G. Stratman, Justin J. Boutilier, Laura A. Albert. 2025-06-23. Ambulance Allocation for Patient-Centered Care. https://arxiv.org/abs/2505.23560

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Subpath-Based Column Generation for Electric Vehicle Routing Problems

Motivated by widespread electrification targets, this paper studies an Electric Vehicle Routing Problem with Time Windows and Nonlinear Charging (EVRPTWNL) that jointly optimizes routing-scheduling decisions and charging decisions given vehicle capacities, time windows and battery capacities. We develop a column generation scheme with a subpath-based label-setting algorithm that decomposes the pricing problem into two phases: (i) generating subpaths between charging stations, and (ii) combining subpaths into paths while optimizing charging decisions in between. We formalize a domination framework to establish the convergence and exactness of the algorithm, and prove that the methodology can solve a range of EVRP variants (e.g., with vehicle capacities, time windows, and nonlinear charging) and relaxation-tightening strategies (e.g., ng-relaxations and subset-row cuts). Computational results show improvements over path-based benchmarks in both computational time and solution quality, especially when time windows become wider, when vehicles can perform multiple tasks on a single charge and when vehicles still need to recharge several times across the planning horizon. Ultimately, the methodology can scale to otherwise intractable instances with up to 100 customers, thereby enhancing fleet management capabilities across electrified logistics areas.

math.OC

Polynomial Scaling is Possible For Neural Operator Approximations of Structured Families of BSDEs

Neural operator (NO) architectures learn nonlinear maps between infinite-dimensional function spaces and are widely used to accelerate simulation and enable data-driven model discovery. While universality results ensure expressivity, they do not address \emph{complexity}: for broad operator classes described only through regularity (e.g.\ uniform continuity or $C^r$-regularity), information-theoretic lower bounds imply that minimax-optimal NO approximation rates scale \emph{exponentially} in the reciprocal accuracy $1/\varepsilon$. This has shifted the focus of NO theory toward identifying additional problem-specific structure, beyond regularity, under which suitably tailored NO architectures can leverage to unlock polynomial scaling in $1/\varepsilon$. We exhibit the first polynomial-scaling regime for NO approximations of solution operators in stochastic analysis; by identifying structured families of \emph{non-Markovian} BSDEs with randomized terminal condition parameterized by the Sobolev-regular terminal condition and by Sobolev-regular additive nonlinear perturbations of the generator. We prove that their solution operator can be approximated (uniformly over the family) by a tailored NO whose number of trainable parameters grows \emph{polynomially} in $1/\varepsilon$. We unlock this polynomial scaling regime by \emph{informing the NO's inductive bias} by factoring out the singular part of the associated semilinear elliptic PDE Green's function and by incorporating the Doléans--Dade exponential of the BSDE's common non-Markovian factor into the NO's decoding layers. As a byproduct, we extend polynomial-scaling guarantees from families of linear elliptic PDEs on regular domains to the semilinear setting.

math.OC

The Competive Spectral Radius of Families of Nonexpansive Mappings

We consider a new class of repeated zero-sum games in which the payoff is the escape rate of a switched dynamical system, where at every stage, the transition is given by a nonexpansive operator depending on the actions of both players. This generalizes to the two-player (and non-linear) case the notion of joint spectral radius of a family of matrices. We show that the value of this game does exist, and we characterize it in terms of an infinite dimensional non-linear eigenproblem. This provides a two-player analogue of Mañe's lemma from ergodic control. This also extends to the two-player case results of Kohlberg and Neyman (1981), Karlsson (2001), and Vigeral and the second author (2012), concerning the asymptotic behavior of nonexpansive mappings. We discuss two special cases of this game: order preserving and positively homogeneous self-maps of a cone equipped with Funk's and Thompson's metrics, and translations of a finite dimensional normed space.

math.OC