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: https://digitalho.com/blog/heor/
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: https://digitalho.com/health-economic-modelling/
Using Economic Modeling in HEOR
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.
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.
Conclusion
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.
