Emergent Dynamical Spatial Boundaries in Emergency Medical Services: A Navier-Stokes Framework from First Principles

TL;DR

This paper introduces a fluid dynamics-based framework to define continuous spatial boundaries for EMS response, revealing demographic disparities and guiding resource allocation to improve emergency response effectiveness.

Contribution

It develops a novel Navier-Stokes equations approach to model EMS response boundaries from first principles, moving beyond traditional ad-hoc GIS methods.

Findings

Response effectiveness decays exponentially over time.

Elderly populations face longer average response times.

The framework identifies vulnerable communities for targeted improvements.

Abstract

Emergency medical services (EMS) response times are critical determinants of patient survival, yet existing approaches to spatial coverage analysis rely on discrete distance buffers or ad-hoc geographic information system (GIS) isochrones without theoretical foundation. This paper derives continuous spatial boundaries for emergency response from first principles using fluid dynamics (Navier-Stokes equations), demonstrating that response effectiveness decays exponentially with time: $\tau(t) = \tau_0 \exp(-\kappa t)$, where $\tau_0$ is baseline effectiveness and $\kappa$ is the temporal decay rate. Using 10,000 simulated emergency incidents from the National Emergency Medical Services Information System (NEMSIS), I estimate decay parameters and calculate critical boundaries $d^*$ where response effectiveness falls below policy-relevant thresholds. The framework reveals substantial demographic heterogeneity: elderly populations (85+) experience 8.40-minute average response times versus 7.83 minutes for younger adults (18-44), with 33.6\% of poor-access incidents affecting elderly populations despite representing 5.2\% of the sample. Non-parametric kernel regression validation confirms exponential decay is appropriate (mean squared error 8-12 times smaller than parametric), while traditional difference-in-differences analysis validates treatment effect existence (DiD coefficient = -1.35 minutes, $p < 0.001$). The analysis identifies vulnerable populations--elderly, rural, and low-income communities--facing systematically longer response times, informing optimal EMS station placement and resource allocation to reduce health disparities.