Clinical Management Using the Science of Health Economics

Ao Wang^*
*Correspondence: Ao Wang, Department of Economics, University of California, Berkeley, CA 94720, USA, Email:

Keywords

Economics education • Decision making • Economics

Introduction

The significant variance in operation rates across doctors with equal financial incentives demonstrates the significance of non-financial motivations. Due to a significant randomised trial, empirical knowledge about price variation facing consumers (demand price) is significantly better than that regarding price variation facing doctors (supply price). The impact of various interventions on health status is also taken into account in a significant body of literature. Due to the size of public spending on healthcare and the speed of technical advancement, economists from many different nations have contributed to the on-going policy discussion on health care [1].

Because health can only be poorly observed and measured, it is plainly challenging to do empirical research on the generation of health capital. However, studies that were prompted by Grossman's study produced significant revelations and proof. In example, it has been demonstrated that adult health and wellbeing are significantly influenced by childhood health and the health services used to improve it, such as prenatal care. Additionally, it has concentrated on the long-term advantages of elements like exercise and weight management as well as the link between alcohol and tobacco addiction and health.

A key distinction in population health analysis is between communicable and no communicable diseases, with the former being much more common in developing nations and the latter currently predominating in developed nations. Early health economics literature implicitly emphasised no communicable diseases and individual choices that have an impact only on one's own health. When it comes to communicable diseases, there are considerable spill over effects since people's activities (or lack thereof) have an impact on other people's health risks. These behaviours have traditionally been assumed in epidemiological models of communicable illnesses. However, since the 1990s, there has been a subfield of research that examines how economic incentives might alter people's decisions that have an impact on the community. The expansion of HIV/AIDS and the growing focus on health care and health policy in developing nations have both contributed to the motivation for this research. As more resources are given to the growing issue of infections that develop treatment resistance, analysis that integrates the modelling of individual motivations and spill over effects will also be important [2].

Description

Various economic evaluation methodologies are used in health economics to assess medical interventions. Cost-effectiveness analyses, cost-utility analyses, cost-minimization analyses, cost-benefit analyses, and costthreshold analyses are the most often utilised types of analysis. All of these methods take into account the direct and indirect expenses of the medical intervention, but they differ in how they determine the effectiveness or utility of the intervention. This chapter describes how economic evaluation is used in genomic medicine, which is similar to other areas of medicine. However, there are still a few crucial factors that must be taken into account at each level of the economic analysis of genetic medicine. These components primarily include the nature of genomic data and the numerous ethical, legal, and societal concerns that these raise. This chapter presents the application of economic evaluation in the post genomic era, where next-generation sequence analysis will become a crucial component of genomic medicine applications, and projects the application of economic evaluation in the post genomic era using the example of warfarin pharmacogenomics [3].

Health of the health economy

Most people consider health economics to be an area of applied economics. Some of the most valuable research merely uses fundamental economic principles but calls for in-depth understanding of medical systems and technology. Many significant policy-relevant articles are published in journals read by doctors and other people directly involved in health, and policy-oriented research plays a significant role in this field. Additionally, it may be argued, has done, that health economics has contributed more than just the application of traditional economic and statistical toolkits. These books give readers plenty of opportunity to assess these [4].

The writers of this article provide a non-exhaustive review of the methods economists have created to calculate inequality and unfairness in health and healthcare. These metrics have their roots in univariate measurements that were created in the early 20^th century to gauge income inequality, like the Lorenz curve and Gini coefficient. Bivariate inequality measures were created by economists as well, specifically for characterising the distribution of different spending categories across income. Some of these early research examined health care expenditures as a part of household expenditure at various income levels using tools like concentration curves and indices. These measurements have just recently been utilised explicitly for health economics applications. Who developed the K index as a proximate measure of healthcare quality, is likely responsible for the first proposal for the application of the Gini coefficient in a health economics context. The purpose of this policy was to punish instances in which preventable morbidity was concentrated in a small number of people rather than being distributed more evenly throughout a population. In his international studies spanning a variety of high and middle income nations, also used the Gini coefficient to evaluate disparities in age at death. However, more contemporary uses of the Gini are less common [5].

Conclusion

The authors' summary, which primarily serves as a catalogue of what is already available, concentrates on the most significant contributions made since 2000. For a more thorough discussion of the literature on the causal mechanisms relating health and income, the authors point readers to the literature list at the end of this article. As a result, they restrict themselves to a brief presentation of the various measurement techniques developed by economists. Four sections make up the remaining portion of this article. In the following section, the writers go over how inequality is measured. The authors then discuss decomposition techniques and provide tools for measuring health disparities. The last portion comes to an end. In the following for the sake of conciseness, the authors refer to health variables.

Acknowledgement

None.

Conflict of Interest

None.

References

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