Economic Modelling in HEOR

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In Health Economics and Outcomes Research (HEOR), economic modeling plays a critical role by providing valuable information about the economic impact of healthcare interventions and policies. This article will introduce you to the basics of economic modeling in HEOR, and provide an overview of the latest trends in healthcare modeling methodologies.


What is HEOR

HEOR (Health Economics and Outcomes Research) is a combination of two fields coming together to provide useful information to health care decision-makers. It is an interdisciplinary field that combines knowledge from economics, healthcare, and statistics to examine the effectiveness, cost, and outcomes of healthcare interventions.

HEOR performs assessment and measurement of the relationship between treatment and actual outcomes to provide evidence-based recommendations for improving the quality of care. This area of research takes into account a wide range of health outcomes of interventions, including not only clinical trials and clinical outcomes but also financial aspects and less specific measures such as quality of life and satisfaction as determined by patients through surveys or extracted from electronic health records. Why these studies are important and how HEOR data are used can be found here:


Types of Economic Models in HEOR

Economic modeling plays an important role in HEOR because it provides tools for analyzing the economic impact of various healthcare interventions and policies. These models help estimate the cost, effectiveness, and outcomes of various healthcare technologies, procedures, and programs.

Economic modeling allows for scenario analyses, predicting future outcomes, and evaluating alternative healthcare strategies. It also enables informed decisions about resource allocation and prioritization in the healthcare system.

HEOR uses different types of economic models for health care analysis and research:

1. Cost projection models, which help estimate the costs of a particular intervention or treatment. They take into account the cost of medical services, drugs, procedures, and other resources needed to implement an intervention.

2. Efficiency Assessment Models. They help determine how well an intervention or treatment achieves the desired clinical and economic outcomes.

3. Microsimulation models. These are used to create virtual simulations that analyze the health and economic impact of different interventions or policies. They help estimate potential outcomes and predict effects over the long term.

4. Decision Trees. These models are used to evaluate different healthcare decision-making strategies. They take into account various factors such as cost, efficiency, quality of life, and other indicators to help make informed decisions in allocating resources and choosing optimal strategies.

5. Markov models that take into account dynamic transitions and long-term effects in health care. They are based on the concept of Markov chains and allow the modeling of patient transitions between different health states, taking into account transition probabilities and length of stay in each state. Markov models are particularly useful for modeling chronic diseases and assessing long-term health outcomes.

6. Discrete Event Modeling. Are used to model complex systems and patient pathways in health care. They take into account the sequence of events and the interaction of various factors in the health care system. Discrete event modeling can help optimize treatment processes, predict resource utilization, and improve quality of care.

7. Dynamic transmission models. These models are used to estimate the impact of infectious diseases and public health interventions. They take into account the spread of disease in a population, including transmission, immunization, and other control measures. Dynamic transmission models help predict epidemiologic trends, evaluate the effectiveness of interventions, and take steps to prevent the spread of disease.

Each type of model has its own advantages and limitations, and the choice of a particular model depends on the specific goals and research questions of HEOR. You can learn more about this here:


Using Economic Modeling in HEOR

Economic modeling plays an important role in HEOR, and is used for a variety of purposes and analyses. The main applications of economic modeling in HEOR are:

1. Cost-effectiveness analysis (CEA) of medical interventions. Economic modeling allows you to estimate the cost and clinical outcomes of different medical interventions and compare them to each other. CEA analysis helps determine which interventions are most cost-effective and achieve the desired outcomes.

2. Budget impact analysis and resource allocation decisions. Economic modeling assesses the financial impact of various health care interventions and policies on the health care system budget and resources. This helps make decisions about resource allocation and the optimal use of limited funding.

3. Evaluation of health care policies and programs. Economic modeling allows you to evaluate the effects and outcomes of various health policies and programs. This may include evaluating the effectiveness of preventive measures, screening programs, introduction of new technologies, etc. Economic modeling helps determine which policies and programs are most effective from a health and economic standpoint.

4. Comparative effectiveness studies and treatment recommendations. Economic modeling allows you to compare different treatments and evaluate their relative effectiveness and cost. This helps develop recommendations for optimal treatment and resource allocation in health care.

5. Cost-based pricing and reimbursement decisions. Economic modeling helps determine reasonable prices for health care services, drugs, and technologies, and develop reimbursement strategies based on cost and expected outcomes. This is important to the affordability and sustainability of the health care system.

Thus, economic modeling, when used in HEOR, assists in making informed health care decisions based on evidence and assessment of economic and clinical outcomes.


Future Directions and Emerging Trends in Health Economic Modeling

Over the past few years, economic modeling in health care has become increasingly advanced and offers more sophisticated methodologies. One major step in this direction is the shift from using Microsoft Excel© software to developing models using programming languages such as R. This transition is driven by the flexibility, efficiency, and power of the code platforms. Another important trend in the development of health economics methodologies is the use of open-source models, which allow for more collaborative and repeatable economic evaluations for new treatments.

However, experts in HEOR research note that there is also considerable resistance to the use of these "advanced" methodologies, which are still not widely used in industry, especially in the context of health technology evaluation. Therefore, a key theme of the ISPOR Europe 2022 conference was to discuss the future directions of "advanced" methodologies. In particular, practical issues related to the wider application of these methodologies were addressed, such as:

1. Transparency of advanced methodologies. It is important that advanced methodologies be transparent enough so that other researchers and decision makers can understand and evaluate their findings and conclusions.

2. Specialist expertise. The question arises as to whether health economists have enough experience and expertise to successfully implement complex methodologies and apply them correctly in real-world situations.

3. Managing conflicts of interest. Situations arise where advanced methodologies may conflict with commercial interests or affect outcomes.

New trends in healthcare economic modeling address many of these issues:

1. Real-world data integration. The future of economic modeling includes greater use of real data on patient health, treatment costs, outcomes, and other factors. This allows for more accurate and relevant models that take into account individual patient characteristics and the healthcare context.

2. Consideration of social and economic factors. Healthcare economic modeling will increasingly focus on accounting for social and economic factors that influence health and treatment outcomes. This includes factors such as social status, education, access to healthcare services, economic inequalities, etc. Taking these factors into account will help to better understand and predict the impact of different policies and interventions on health and costs.

3. Using Artificial Intelligence and Machine Learning. Artificial intelligence (AI) and machine learning (ML) represent great potential for advancing economic modeling in health care. These technologies can help automate the modeling process, analyze large amounts of data, optimize decision-making, and predict treatment outcomes. The use of AI and MO in economic modeling can significantly improve its accuracy, transparency, and efficiency.

4. Hybrid models and complex systems. Instead of using individual models and simplified assumptions, the future of economic modeling includes the development of hybrid models and integrated systems that integrate different aspects of health, economics, and social factors. This will allow for greater consideration of the interactions and dependencies between the various variables and factors in the health care system.

5. Accounting for long-term effects and values. In the future, economic modeling will pay more attention to accounting for the long-term effects and costs of treatment. This includes estimating the long-term effects of treatment, the prevention of complications, potential reductions in life expectancy, etc. Consideration of long-term effects and costs will enable more informed decisions about healthcare resources and policies. 

These directions and trends reflect the development of economic modeling in health care to improve its relevance, accuracy, and consideration of the variety of factors affecting health and costs.



Economic modeling plays a vital role in HEOR research by providing valuable information about the economic impact of health care interventions and policies. It establishes and measures the relationship between treatment and actual outcomes, providing evidence-based recommendations for improving care.

HEOR uses different types of economic models, including decision trees, Markov models, microsimulation models, discrete event modeling, and dynamic transfer models. Each type of model provides unique benefits and allows you to consider different aspects of the impact of healthcare interventions and policies.

The use of economic modeling in HEOR has a wide range of applications, including cost-effectiveness analysis of healthcare interventions, budget and resource allocation impact analysis, healthcare policy and program evaluation, comparative effectiveness studies and treatment recommendations, and value-based pricing and reimbursement decisions.

In the future, economic modeling in health care will evolve toward more advanced methodologies, such as the use of programming languages, open-source models, and more consideration of the variety of factors affecting health and costs. This will allow for more accurate and relevant assessment of the impact and effectiveness of various healthcare interventions and policies.

By understanding the economic modeling features of HEOR, researchers, policymakers, and healthcare stakeholders can make informed decisions and efficiently allocate resources. Robust economic models provide valuable information about the economic impact of healthcare interventions, helping to improve patient outcomes and healthcare efficiency.

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