System dynamics simulation for evaluating implementation strategies of genomic sequencing: tutorial and conceptual...

Area of Science:

• Health Policy
• Computational Biology
• Oncology

Background:

• Precision medicine (PM), particularly in oncology, relies on genomic features for complex diagnostic and treatment pathways.
• Evaluating and making decisions for PM necessitates advanced modeling techniques due to inherent complexity.
• System Dynamics (SD) possesses strong modeling capabilities but is underutilized in PM and personalized treatment.

Purpose of the Study:

• To explain System Dynamics (SD) tools within a cancer context.
• To establish the rationale for using SD in genomic testing and personalized oncology.
• To develop a conceptual model for strategic decision-making in Whole Genome Sequencing (WGS) implementation.

Main Methods:

• Explanation of SD tools with cancer-specific examples.
• Comparison of SD with other Dynamic Simulation Modelling (DSM) methods, highlighting SD's advantages.
• Development of a conceptual model using Causal Loop Diagram (CLD) for WGS implementation strategy.

Main Results:

• SD is suitable for health policy evaluation and modeling precision oncology and genomic testing.
• SD's system-oriented approach captures complex dynamics using feedback loops.
• SD models are resource-efficient, enabling exploratory and explanatory analyses over time.

Conclusions:

• SD offers significant advantages for modeling and evaluating complex health systems, especially those with limited data and requiring interpretability.
• SD's ability to model dynamic interactions and feedback loops makes it valuable for precision oncology and genomic testing.
• The conceptual model demonstrates SD's utility for strategic decisions in implementing technologies like Whole Genome Sequencing.