Modelling and performance analysis of clinical pathways using the stochastic process algebra PEPA

Area of Science:

• Health Informatics
• Operations Research
• Biomedical Engineering

Background:

• Hospitals face challenges managing numerous patients with limited resources while maintaining quality.
• Clinical pathway informatics offers solutions but lacks precise mathematical models.
• Existing descriptions of clinical pathways are vague, hindering effective management and optimization.

Purpose of the Study:

• To introduce a novel mathematical model and platform for clinical pathway management.
• To enable quantitative analysis of clinical pathway performance.
• To address the limitations of current vague descriptions in clinical pathway research.

Main Methods:

• Development of the Imperial Clinical Pathway Analyzer (ICPA) platform.
• Extension of the stochastic model performance evaluation process algebra (PEPA) to create clinical pathway PEPA (CPP).
• Simulation of clinical pathway stochastic behaviors using CPP and data from public clinical databases.

Main Results:

• ICPA effectively models clinical pathways, enabling quantitative performance analysis.
• Demonstrated cost reduction by identifying and removing redundant resources.
• Provided accurate estimation of patient passage times and maximum patient throughput.

Conclusions:

• ICPA is an effective platform for comprehensive clinical pathway management.
• The platform facilitates understanding of complex pathway components (states, activities, resources, constraints).
• ICPA supports performance analysis, aiding hospitals in optimizing time and resource utilization.