AI for Health Economics Modeling
What you'll learn
- 1Use AI to accelerate the development of cost-effectiveness and budget impact models
- 2Apply AI to identify and validate key model inputs from clinical and economic literature
- 3Build prompts that help structure Markov models, partitioned survival analyses, and microsimulations
- 4Understand how AI can assist with probabilistic sensitivity analysis and scenario modeling
# AI for Health Economics Modeling
Health economics models translate clinical trial data into the language payers understand: cost per QALY, incremental cost-effectiveness ratios (ICERs), budget impact projections, and value-based pricing analyses. These models are central to HTA submissions worldwide.
Types of Models and AI Applications
Cost-Effectiveness Models (CEA): - Markov models with defined health states and transition probabilities - Partitioned survival models using clinical trial Kaplan-Meier data - Discrete event simulations for complex patient pathways - Microsimulations for heterogeneous patient populations
Budget Impact Models (BIM): - Estimate the financial impact of adopting a new therapy on a payer's budget - Require epidemiology data, market share projections, and treatment cost comparisons
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What you'll learn:
- Use AI to accelerate the development of cost-effectiveness and budget impact models
- Apply AI to identify and validate key model inputs from clinical and economic literature
- Build prompts that help structure Markov models, partitioned survival analyses, and microsimulations