Using a game-theoretic approach to design optimal health insurance for chronic disease

Ho, Ting-Yu; Fishman, Paul; Zabinsky, Zelda B.

doi:10.1080/24725579.2019.1567626

Cited by 5 publications

(4 citation statements)

References 55 publications

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“…Furthermore, comprehensive statistical analysis is provided to establish a belief that the results generated are not by chance or involve any biases. [21] presented a comprehensive game-theoretic model considering the association between cost-sharing and dynamic health outcomes for chronic heart disease by applying Stochastic Stackelberg Game (SSG). SSG needs to have all the information and knowledge of the player's strategies to make the decision.…”

Section: Discussionmentioning

confidence: 99%

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Disease Classification in Health Care Systems With Game Theory Approach

Raja

Asghar

2020

IEEE Access

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There are numerous cases in real life when we come across problems involving the optimization of multiple objectives simultaneously. One of the complexities of solving such problems is that often one or more objectives are usually conflicting under given conditions. In this study, the benefits of relying on a deployed Clinical Decision Support System (CDSS) concerning the overall reputation of a health facility has been studied. The analysis is performed in terms of a cooperative Bayesian game-theoretic model. The game is played between two players of which the first player is a patient who needs quick and accurate medical attention and the second player is the hospital administration that relies on medical experts as well as integrated multi-objective clinical data classification systems for decision-making. The proposed model ''MEAF''-Multi-objective Evolutionary Algorithm using Fuzzy Genetics attempts to address accuracy and interpretability simultaneously using Evolutionary Algorithms (EAs). This model enables a H CDSS to detect a disease accurately by using the available resources efficiently. The results of our simulation show that H CDSS produces better and accurate results in detecting disease with efficient resource utilization along with the reduced computational cost. This approach has also produced a better response for both players based on Bayesian Nash Equilibrium. Finally, the proposed model has been tested for accuracy, efficient resource utilization, and computationally cost-effective solution.

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Section: Discussionmentioning

confidence: 99%

“…The framework proposed in [21] is an attempt to analyze cost-sharing health policies for individuals with chronic disease. This new framework is based on a game-theoretic scheme known as the Stochastic Stackelberg Game (SSG).…”

Section: Related Workmentioning

confidence: 99%

Disease Classification in Health Care Systems With Game Theory Approach

Raja

Asghar

2020

IEEE Access

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“…The problem on health insurance or healthcare has been studied in various papers from various perspectives. For example, Phelps [19] provided theoretical analysis and empirical estimates of demand for health insurance; Besley [20] explored the trade-off between risk sharing and incentives to consume increased medical care inherent in reimbursement insurance; Blomqvist [21] investigated the properties of optimal non-linear insurance schedules; Hall and Jones [22] analyzed the rise in health spending; Ellis et al [23] examined the efficiency-based arguments for second-best optimal health insurance with multiple treatment goods and multiple time periods; Gerfin [24] discussed the effects of cost sharing instruments of modern health insurance on healthcare demand; Ho et al [25] studied the optimal health insurance design for chronic diseases; and Zheng et al [26] examined moral hazard and adverse selection effects in healthcare utilization using hospital invoice data. All the aforementioned research works have paid great attention to the examination of "excess health insurance," optimal income taxation, explanation of health spending from the economic point of view, moral hazard and adverse selection, etc.…”

Section: Introductionmentioning

confidence: 99%

Optimal Health Insurance and Trade-Off between Health and Wealth

Zhang

2020

Journal of Applied Mathematics

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Health insurance is considered to be a special type of nonlife insurance with two important features. First, compared with property insurance, health insurance provides valuable hedge against unpredictable shocks to health status, instead of loss on property. Therefore, a modified utility function that describes the trade-off between health and wealth should be applied in optimal indemnity design. Second, in the case that the insured is severely or critically ill, with necessary medical treatment, the insured may not fully recover from an illness or an injury. The doctor usually communicates with the patient to set up a personalized treatment plan and explains clearly about the expected outcome beforehand. Hence, there is some probability that health insurance helps to rescue the insured from disastrous financial burden, but it still yields a lower utility of health. By taking these special features into account, we formulate the optimization problem and characterize the optimal solutions via the Lagrange multiplier method and optimal control technique. Finally, we examine our optimal contracts by numerical illustration. Our research work gives new insights into health insurance design.

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“…Pioneered by Arrow [1,2,3], many articles on optimal insurance problem under various constraints, with consideration of application in personal lines or commercial lines, have been contributed to the literature in this field. Typical papers include but are not limited to Smith [31], Mossin [25], Spence and Zeckhauser [32], Raviv [30], Doherty and Schlesinger [8], Gollier and Schlesinger [16], Young [37], Moore and Young [24], Lee [19], Zhou and Wu [42], Zhou et al [43], Bernard et al [4], Promislow and Young [28], Lu et al [22], Xu et al [35], Ho et al [18], Fan [11] and Zhang and Wu [40]. Most of the aforementioned papers assume that the individual would suffer a random loss and pay premium to the insurer for indemnity in case that the loss occurs.…”

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confidence: 99%

Optimal health insurance with constraints under utility of health, wealth and income

Zhang¹,

Wu²,

Yao³

2022

JIMO

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We consider an optimal health insurance design problem with constraints under utility of health, wealth and income. The preference framework we establish herein describes the trade-off among health, wealth and income explicitly, which is beneficial to distinguish health insurance design from other nonlife insurance designs. Moreover, the work takes into account the case that if the insured is severely or critically ill, the insured may not fully recover even after necessary medical treatment. By taking these special features into account, the health insurance design problem is formulated as a constrained optimization problem, and the optimal solutions are derived by using the Lagrange multiplier method and optimal control technique. Finally, two numerical examples are given to illustrate our results. Our research work gives new insights into health insurance design.

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Using a game-theoretic approach to design optimal health insurance for chronic disease

Cited by 5 publications

References 55 publications

Disease Classification in Health Care Systems With Game Theory Approach

Disease Classification in Health Care Systems With Game Theory Approach

Optimal Health Insurance and Trade-Off between Health and Wealth

Optimal health insurance with constraints under utility of health, wealth and income

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