Modeling disease progression and treatment pathways for depression jointly using agent based modeling and system...

Area of Science:

• Health Services Research
• Computational Modeling
• Mental Health

Background:

• Depression care pathways and clinical progression are complex and often studied in isolation.
• Understanding the interplay between patient journey and healthcare system demand is crucial but rarely addressed.

Purpose of the Study:

• To develop and present a novel hybrid simulation modeling framework.
• To integrate agent-based modeling for disease progression with system dynamics for care pathways.
• To investigate the bidirectional impact of depression progression and healthcare service access.

Main Methods:

• Developed a hybrid simulation framework combining Agent Based Modeling (ABM) and System Dynamics (SD).
• Synchronously linked ABM and SD to model depression progression and care pathways.
• Parametrized the framework using clinical and service level data from Wales, UK.

Main Results:

• The framework quantifies demand, service capacities, and costs across depression care pathways.
• Evaluated scenarios including varying service provision and cost-effectiveness of early community-based treatment.
• Demonstrated potential to reduce progression to severe depression and lower specialist care costs.

Conclusions:

• The hybrid simulation framework offers a unique approach to studying complex mental health systems.
• It provides valuable insights for optimizing depression care pathways and resource allocation.
• Supports evidence-based decision-making for improving mental health service delivery and patient outcomes.