Introduction

Economic modeling plays a critical role in healthcare decision-making, enabling organizations to assess cost-effectiveness, resource allocation, and financial sustainability. As healthcare costs rise and budgets tighten, decision-makers must use data-driven approaches to optimize spending, evaluate treatment interventions, and forecast healthcare outcomes.

This five-day advanced training course is designed to equip healthcare policymakers, economists, finance professionals, and analysts with theoretical knowledge and practical skills in economic modeling for evidence-based decision-making. Participants will explore cost-effectiveness analysis (CEA), budget impact models (BIM), Markov models, and machine learning applications in economic forecasting.

Course Objectives

By the end of this course, participants will:

• Understand key economic models used in healthcare decision-making
• Learn cost-effectiveness analysis (CEA), cost-utility analysis (CUA), and cost-benefit analysis (CBA)
• Develop budget impact models (BIM) for healthcare interventions
• Apply Markov models and Monte Carlo simulations in healthcare economics
• Utilize real-world evidence (RWE) and big data in economic modeling
• Evaluate pricing and reimbursement strategies using health economics methods
• Gain hands-on experience with modeling software and data analytics tools

Who Should Attend?

This course is ideal for:

• Healthcare policymakers and government officials
• Health economists and financial analysts
• Hospital administrators and healthcare executives
• Pharmaceutical and medical device industry professionals
• Insurance and reimbursement specialists
• Public health researchers and consultants
• Data scientists working in healthcare economic modeling

Course Outline

Day 1: Foundations of Healthcare Economic Modeling

Introduction to Economic Modeling in Healthcare

• The role of economic modeling in healthcare decision-making
• Key economic concepts: opportunity cost, marginal analysis, and utility theory
• Comparing cost-effectiveness, cost-utility, and cost-benefit analyses
• Case study: Economic modeling in global healthcare policy decisions

Basic Costing Methods and Economic Evaluation

• Direct vs. indirect costs in healthcare economic models
• Cost categories: fixed vs. variable costs, capital vs. operational costs
• Discounting and sensitivity analysis in healthcare cost modeling
• Case study: Cost analysis of a new pharmaceutical intervention

Workshop: Developing a Basic Cost-Effectiveness Analysis (CEA) Model

• Participants will construct a simple cost-effectiveness model for a treatment

Day 2: Advanced Healthcare Economic Evaluation Techniques

Cost-Effectiveness Analysis (CEA) and Cost-Utility Analysis (CUA)

• Measuring incremental cost-effectiveness ratios (ICERs)
• Understanding Quality-Adjusted Life Years (QALYs) and Disability-Adjusted Life Years (DALYs)
• Economic evaluation frameworks used by NICE, WHO, and payers
• Case study: Applying ICERs in drug pricing decisions

Budget Impact Modeling (BIM) for Healthcare Programs

• Developing BIM models to assess financial sustainability
• Understanding the relationship between BIM and CEA
• Case study: Budget impact of introducing a new medical technology

Workshop: Conducting a Cost-Utility Analysis (CUA) for a Healthcare Program

• Participants will build a CUA model using QALYs and ICERs

Day 3: Modeling Techniques and Simulation Methods

Markov Models and Monte Carlo Simulations in Healthcare

• Using Markov Chains to model disease progression
• Monte Carlo simulations for risk and uncertainty analysis
• Case study: Using Markov modeling to evaluate chronic disease treatments

Decision Tree Models in Healthcare Economic Evaluation

• Understanding probabilistic decision trees in treatment analysis
• Decision tree vs. Markov models: when to use each approach
• Case study: Comparing surgical vs. non-surgical interventions using decision trees

Workshop: Building a Markov Model for Chronic Disease Management

• Participants will construct a Markov model using healthcare data

Day 4: Big Data, AI, and Real-World Evidence in Economic Modeling

Leveraging Real-World Evidence (RWE) for Economic Decision-Making

• Sources of real-world data (EHRs, claims data, registries, patient-reported outcomes)
• How real-world evidence supports pricing, reimbursement, and policy
• Case study: Use of RWE in post-market drug evaluations

Artificial Intelligence and Machine Learning in Economic Modeling

• AI-driven predictive modeling for cost forecasting and patient outcomes
• Applying machine learning to healthcare resource allocation and efficiency
• Case study: AI-powered economic modeling for hospital budget optimization

Workshop: Integrating AI and RWE into Economic Modeling

• Participants will use machine learning techniques for economic decision-making

Day 5: Policy Implications, Pricing Strategies, and Final Project

Healthcare Pricing, Reimbursement, and Policy Decision-Making

• Value-based pricing and outcome-based payment models
• Economic modeling in health insurance reimbursement decisions
• Policy implications of economic evaluation in drug pricing and technology adoption
• Case study: Price negotiations based on cost-effectiveness models

Ethical Considerations and Future Trends in Economic Modeling

• Balancing cost-containment with patient access to healthcare
• Addressing equity concerns in healthcare economic evaluations
• The future of predictive modeling, blockchain, and AI in healthcare finance
• Case study: Ethical dilemmas in healthcare economic decision-making

Final Project: Developing a Full Economic Model for a Healthcare Intervention

• Participants will create and present an economic model for a real-world healthcare decision
• Expert panel feedback and discussions

Closing Session and Certification

• Key takeaways and best practices in healthcare economic modeling
• Awarding of Certification of Completion