An approximate optimistic parallel simulator for large-scale primary healthcare systems

Abstract

Primary healthcare (PHC) systems face unsustainable pressure from population aging and the burden of chronic diseases, creating critical workflow inefficiencies and affecting patient care. This challenge particularly affects chronic patient follow-up, where case managers are often overwhelmed by excessive workloads and the complexities of care coordination. In this context, discrete-event simulation (DES) is essential for evaluating the impact of new health policies before implementation. However, simulating these processes at scale entails high computational costs, limiting the feasibility of standard DES approaches for exploring large-scale PHC system scenarios. To address this challenge, we propose a parallel discrete event simulation (PDES) framework designed explicitly for PHC systems. Our proposal implements an approximate parallel simulation protocol based on the bulk synchronous parallel (BSP) model, enhancing efficiency by simplifying event ordering and eliminating rollback. Validation was conducted by the assessment of healthcare experts. We show that this approach achieves significant scalability (up to a 6.95-fold speedup) while maintaining high fidelity. More than 90% of the tested parallel configurations produced results identical to sequential models, with all outcomes remaining clinically accurate. This validates the protocol’s use for strategic public health decision-making.