Modelling competition in health care markets as a complex adaptive system: an agent-based framework

Area of Science:

• Health economics
• Complex adaptive systems science
• Computational modeling

Background:

• Health market reforms require ongoing assessment of regulations and policies.
• Methodological limitations often hinder understanding of market competition's complex dynamics and behavioral changes.

Purpose of the Study:

• To draw parallels between health care markets (HCM) and complex adaptive systems (CAS).
• To propose an agent-based modeling (ABM) framework for investigating competition within HCM.
• To address limitations in current methodologies for analyzing health care market competition.

Main Methods:

• Conceptualizing health care markets as complex adaptive systems.
• Developing an agent-based framework detailing agents, environment, interactions, and attributes.
• Formalizing agent roles and modules to simulate health care market dynamics.
• Identifying data sources and face-validity procedures for the ABM.

Main Results:

• The proposed framework operationalizes the conceptualization of competition in HCM for CAS assessment.
• It provides a structured approach to modeling heterogeneous agents and emergent behaviors in health care markets.
• The framework is designed to recreate the dynamics of health care market competition.

Conclusions:

• The complex adaptive systems approach, particularly agent-based modeling, offers a valuable method to complement existing studies on health care market issues.
• Advancements in data and computational power support the application of CAS for analyzing health care markets.
• This framework enhances the ability to study pressing health care market challenges through computational simulation.