Health care tends to have limited resources, such as medical personnel, drugs, equipment, and funding. Therefore, decision makers are always faced with the problem of prioritization. Prioritization helps to equitably allocate resources to provide the greatest benefit to society as a whole. It also helps ensure that those who need urgent medical care get it first, which can save lives.
In turn, prioritizing health care involves complex ethical issues related to balancing health care and equity. For example, low-income people, children, pregnant women and the elderly may have greater health care needs and may be more vulnerable to disease. They may also have limited access to health resources. Prioritization should take into account the needs of different populations.
Although these principles are important in setting priorities, they can be difficult to implement because of a lack of clarity about how they should be applied and how they interact with each other. The use of health economics models can significantly help to address this problem.
Economic models are also an essential part of pharmaceutical market access and HEOR.
Types of Health Economics Models
Economic modeling in health care is the process of developing and using numerical models that estimate and predict the cost and effectiveness of health care technologies, programs, and policies. Economic modeling in health care helps improve health care decision-making by considering the many factors related to the cost and effectiveness of health care technologies and programs.
There are different types of economic models in health care, including mathematical models, Markov models, decision-making models, stochastic models, agent-based models, and others. The models most commonly used for outcome analysis are:
- Cost-effectiveness Models (CEM) is a method used to estimate which medical interventions may provide the greatest benefit compared to the cost of providing them. It allows different medical interventions to be compared in terms of their cost and effectiveness.
- Cost Utility Analysis (CUA) is a method used to evaluate the effectiveness of various medical interventions by comparing their impact on patients’ quality of life, vital activities, and health.
- Budget Impact Models (BIM) is a method used to estimate the impact of medical technologies and programs on a healthcare organization’s budget. It estimates the cost of implementing new technologies and programs, as well as their impact on the health care budget as a whole.
Using economic models of health care in resource allocation and priority setting
Health economic models can be used to allocate resources and prioritize health care. These models help determine which medical technologies and programs will be most efficient and cost-effective in the use of limited resources.
One of the primary methods of allocating resources in health care is through the use of prioritization criteria. This may include consideration of the following factors:
- severity of disease;
- likelihood of a positive outcome;
- cost of treatment;
- availability of health care services.
Economic models can help in formally evaluating these factors, and in determining the most efficient and optimal allocation of resources.
Another important application of economic models in health care is in prioritizing research. Economic models can help determine which research will be most useful and effective in achieving desired outcomes, and in using limited resources most efficiently.
In addition, economic models can help assess the effectiveness of new medical technologies and programs by allowing comparison of their cost and effectiveness with other treatment options. This can help in making decisions about which technologies and programs should be implemented in health care and what resources should be allocated for their implementation.
Challenges of using health economic models to allocate resources and prioritize
Although health economic models can be useful in determining resource allocation and priorities in health care, there are some problems associated with their use:
- Lack of model accuracy. Modeling in healthcare is a complex and multifactorial process, and even the most advanced models cannot always account for all factors and variables. This can lead to insufficient accuracy and unreliability of simulation results.
- Failure of models to account for context and individual differences. Health care models generally cannot account for individual patient differences and the context in which research is conducted. This may limit their ability to predict the effectiveness and cost of medical technologies and programs in a particular region or population.
- Uncertainty in model inputs and assumptions. Data uncertainty issues can arise because of limited data availability, ambiguous interpretations of research results, or improper application of statistical methods. In some cases, economic health models may be based on analyses of data that are not sufficiently representative of the target population or do not account for significant factors. In addition, model assumptions can introduce significant errors into the results. Assumptions can be made about what data to use, what variables to consider, how to estimate treatment costs and effectiveness, how to account for various risks, etc. If these assumptions turn out to be incorrect, model results may be unreliable and may not match reality.
- Lack of transparency and accessibility. Many health economics models are developed and used by experts and health professionals, and they are often not available to the general public. This can create problems in understanding what factors and criteria were used in the modeling and what conclusions were drawn from the models.
- Health care economic models typically do not take into account patient preferences and opinions, which can lead to inadequate consideration of quality of life and other important aspects of health care that are important to patients.
Examples of Health Economics Models for Resource Allocation and Prioritization
Rising costs and limited resources are driving health systems to improve how they make decisions about resource allocation. This study, published by BMC, which is part of Springer Nature, January 07, 2021, determined which priority setting and resource allocation (PSRA) methods were used from 2007-2019 in health systems in high-income countries.
The study found that formal structures for planning and evaluating health care services were applied at virtually all levels of government and administration, including:
- national;
- state;
- regional;
- health facility level.
They have been used to prioritize a wide range of health services, from primary care to mental health, drug reimbursement, immunizations, and treatment for specific diseases.
One important point is that most PSRA-related interventions have been conducted in countries with a predetermined cost limit, and organizations with fixed budgets must find ways not to exceed that limit. In other words, the total cost of health care services provided per year is largely pre-determined as a fixed bundle, and providers must choose priorities based on anticipated needs or modify their reimbursement structure to keep total costs within the bundle.
According to the study, welfare economics approaches have been used extensively in prioritizing and recommending coverage of specific technologies. Specifically, cost-effectiveness analysis and cost-utility analysis have been used by HTA agencies around the world-CADTH in Canada, PBAC and MSAC in Australia, PHARMAC in New Zealand, and NICE in the United Kingdom. In this way, priority-setting approaches based on the science of decision-making have proven useful and versatile in allocating limited resources at a wide variety of levels of management and administration.
Future Directions for Health Economics Models in Resource Allocation and Prioritization
There are several possible directions in the development of health economics models that could be used to allocate resources and determine priorities in the future:
- Using more sophisticated economic models to estimate costs and benefits. This includes developing models that can account not only for the immediate costs of care, but also for the broader social and economic benefits associated with improving population health.
- Developing better tools for assessing the quality and effectiveness of health care services. This can help improve assessment of which health services are most effective and which are worth prioritizing when allocating resources.
- Using technology and data analytics to improve forecasting of costs and resource needs. This includes using artificial intelligence and machine learning to analyze large amounts of population health and health services data to improve prediction of future resource needs and determine optimal resource allocation.
- Using new sources of patient health data-such as data from mobile apps or wearable devices. This can help improve forecasting of costs and treatment outcomes.
- Consideration of patient preferences and values can be an important factor in resource allocation and prioritization decisions. For example, economic models can take patients’ preferences for different treatment modalities and factor them into resource allocation. This can help improve treatment quality and patient satisfaction.
- Developing new models of health care financing. This could include moving away from the traditional volume-based funding system to new models that incentivize better quality and more efficient use of resources.
- Improving the coordination and integration of health care services. This can help reduce the cost of duplication of services and improve treatment outcomes. In this case, economic models can be used to estimate the costs and benefits of different models of care coordination and integration.
- Greater transparency and stakeholder participation can increase the usefulness of health economic models for decision making. For example, peer reviews can be used to obtain feedback from experts and stakeholders on the modeling outcome and its applicability in the real world. Involving patients and members of the public can help improve understanding of how various decisions can affect public health and increase public support for such decisions.
Conclusion
Health economic models have been successfully used to evaluate the effectiveness and cost of various treatment modalities, to identify factors that affect the health of a population, and to determine the most effective strategies for preventing and treating disease. Investment in health economic modeling can help economic agencies and governments make more informed decisions about resource allocation and prioritization in health care. This, in turn, can improve the quality of healthcare and make treatment more accessible to all.
