Related Experiment Video
Updated: Oct 9, 2025
Prehospital Thrombolysis: A Manual from Berlin
Published on: November 26, 2013
Modelling STEMI service delivery: a proof of concept study
Justin Cole1,2, Richard Beare2,3, Thanh Phan4
1Cardiology Unit, Department of Medicine, Peninsula Health, Frankston, Victoria, Australia.
Insights
Optimizing access to percutaneous coronary intervention (PCI) centers for ST-elevation myocardial infarction (STEMI) patients is crucial. Predictive modeling can improve patient travel times and healthcare resource allocation.
Area of Science:
- Cardiology
- Health Services Research
- Geographic Information Systems
Background:
- Traditional access to percutaneous coronary intervention (PCI) centers relies on outdated referral patterns and arbitrary geographic boundaries.
- ST-elevation myocardial infarction (STEMI) patient access to timely treatment is critical for outcomes.
- Predictive modeling offers a novel approach to optimize PCI center accessibility.
Purpose of the Study:
- To develop predictive models for ST-elevation myocardial infarction (STEMI) demand.
- To assess time-efficient access to percutaneous coronary intervention (PCI) centers.
- To evaluate the impact of PCI center network configuration on patient access and hospital loading.
Main Methods:
- Utilized Google Maps API to estimate travel times from random addresses to PCI centers in Melbourne, Australia.
- Compared estimated travel times with real-world ambulance data, accounting for peak hour traffic.
- Modeled STEMI incidence per postcode and assessed the effect of network changes on access within 30 minutes.
Main Results:
- Approximately 10% of STEMI cases exceeded a 30-minute travel time to a PCI center.
- Removing outer metropolitan PCI centers increased travel times for nearly 20% of STEMI cases.
- A 7-center model showed comparable performance to the existing 11-center network, with high correlation between estimated and actual travel times (0.82).
Conclusions:
- Developed a framework integrating prehospital data, healthcare resources, and health statistics to model STEMI demand and PCI access.
- Methodology allows objective assessment and optimization of healthcare resource allocation for STEMI care.
- The model is adaptable for incorporating additional variables to enhance healthcare efficiencies.
Background:
Access to individual percutaneous coronary intervention (PCI) centres has traditionally been determined by historical referral patterns along arbitrarily defined geographic boundaries. We set out to produce predictive models of ST-elevation myocardial infarction (STEMI) demand and time-efficient access to PCI centres.
Methods:
Travel times from random addresses to PCI centres in Melbourne, Australia, were estimated using Google map application programming interface (API). Departures at 08:15 and 17:15 were compared with 23:00 to determine the effect of peak hour traffic congestion. Real-world ambulance travel times were compared with estimated travel times using Google map developer software. STEMI incidence per postcode was estimated by merging STEMI incidence per age group data with age group per postcode census data. PCI centre network configuration changes were assessed for their effect on hospital STEMI loading, catchment size, travel times and the number of STEMI cases within 30 min of a PCI centre.
Results:
Nearly 10% of STEMI cases travelled more than 30 min to a PCI centre, increasing to 20% by modelling the removal of large outer metropolitan PCI centres (p<0.05). A model of 7 PCI centres compared favourably to the current existing network of 11 PCI centres (p=0.18 (afternoon), p=0.5 (morning and night)). The intraclass correlation between estimated travel times and ambulance travel times was 0.82, p<0.001.
Conclusion:
This paper provides a framework to integrate prehospital environmental variables, existing or altered healthcare resources and health statistics to objectively model STEMI demand and consequent access to PCI. Our methodology can be modified to incorporate other inputs to compute optimum healthcare efficiencies.
