Real-Time Adaptive Allocation of Emergency Department Resources and Performance Simulation Based on Stochastic Timed Petri Nets

Overcrowding in emergency departments (EDs) is a common problem encountered by healthcare systems worldwide. Its essence is the imbalance between the need for emergency care and available resources, such as doctors, nurses, medical supplies, and treatment facilities and spaces. Such an imbalance increases with the volume of visiting patients. To solve the problem of ED overcrowding, service providers need to ensure rational allocation of resources in the emergency process to the greatest extent. This article uses stochastic timed Petri nets (STPNs) as a modeling and simulation tool to optimize the resource allocation in the emergency care workflow. On the basis of STPN simulation architecture, we propose a novel “observation-response” block (ORB) to adaptively supplement the corresponding resources according to the local crowding situation in the emergence workflow, so as to reduce the waiting time of patients in urgent need of treatment. In this article, models of patient arrival, triage, and examination process are constructed. Then, considering the waiting time of patients as the optimization objective, the statistical simulation based on STPN models is performed to verify the effectiveness of the proposed ORB block in the emergency workflow resource optimization process. The presented work provides a feasible way for the optimal ED resource allocation.