Pre-hospital emergency network serves as a major gateway to the Accident and Emergency (AE) department of hospitals which is affected by overcrowding, and majority of projects studying pre-hospital emergency care network deal only with ambulance or fireman localisation as a means of addressing the problems confronting the Accident and Emergency (AE) department. However, this research work studied in detail the patient flow through the pre-hospital emergency care network. Taking into account not only fireman and private ambulances localisation, but also the other services performed in this network such as Emergency call service the service of the Emergency response team using Timed Coloured Petri Nets.

In developing a Timed Coloured Petri Nets (TCPN) model for a pre-hospital emergency care network, the emergency service of the State of Osun (O’ambulance) was used as a case study. The developed pre-hospital emergency care network (TCPN) model consists of five sub modules. Arrival sub module modelled the arrival of emergency calls with different health cases. Sort call sub module modelled the call desk and how emergency calls are received by the available call desk agent (perm). Perm Decision sub-module modelled the decision of call desk agent (perm) based on the emergency cases. Rescue team (RT) Decision sub module modelled the decision and intervention of Rescue team. While the Rescue-to-hospital sub-module modelled the transportation of victims to hospital. The developed TCPN model was simulated using CPN tools. The validation of the developed TCPN model was explored by carrying out the statistical analysis between the simulated and the observed emergency call cases under study.

The simulation results of the developed TCPN model revealed the number of emergency cases that require immediate and urgent response as 2, 6 and 19 while those that require no immediate response as 8, 22, 64 for the first, second and third simulations respectively. Revealing that some emergency calls require no immediate response and patients do not need to be taken to the hospital. Statistically, there were no significant difference between the simulated and the observed number of emergency cases that require immediate and urgent response.

Conclusively, this research work has been able to develop a TCPN model for studying a pre-hospital emergency care network. Also, the developed Timed Coloured Petri Nets model, through its simulation, could help in predicting the number of patients that do not need to be taken to the emergency department hence, reducing overcrowding in hospitals.