Evolutionary game theory on over-treatment behavior under drug-proportion regulation

WU Xiaodan, LIU Yifan, LI Juan, CHU Chao-Hsien

Systems Engineering - Theory & Practice ›› 2019, Vol. 39 ›› Issue (12) : 3163-3175.

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Systems Engineering - Theory & Practice ›› 2019, Vol. 39 ›› Issue (12) : 3163-3175. DOI: 10.12011/1000-6788-2018-1930-13

Evolutionary game theory on over-treatment behavior under drug-proportion regulation

  • WU Xiaodan1, LIU Yifan1, LI Juan1, CHU Chao-Hsien2
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Abstract

Drug-proportion regulation is on implementing following the price regulation and the elimination of markups on pharmaceuticals to curb the over-treatment, which is an important issue for Chinese healthcare reform. Based on that, we propose an evolutionary game model of doctor-patient behavior under drug-proportion regulation. Theoretically, it founds that there exists behavioral evolutionary law and stable strategies between physicians and patients according to replicator dynamics equation. Then treatment strategies are analyzed by considering normal treatment costs, the ratio of over-treatment to normal treatment costs, physician performance coefficient, and the severity of illness. The results show that drug-proportion regulation does not always inhibit over-treatment, which depends on a transition point. Physicians prefer to choose overtreatment while the drug-proportion is lower than the transition point. It is worth noting that the severity of illness affects over-treatment under the drug-proportion regulation. Physicians are prefer to over-treatment when patients are less ill. These conclusions are beneficial for drug-proportion setting, light-illness monitoring, hierarchical diagnosis, and information disclosure mechanism. Finally, related healthcare policy suggestions are provided.

Key words

evolutionary game / medical expenses / over-treatment / drug-proportion

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WU Xiaodan , LIU Yifan , LI Juan , CHU Chao-Hsien. Evolutionary game theory on over-treatment behavior under drug-proportion regulation. Systems Engineering - Theory & Practice, 2019, 39(12): 3163-3175 https://doi.org/10.12011/1000-6788-2018-1930-13

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Funding

National Social Science Foundation of China (17BGL087)
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