Tripartite Evolutionary Game and Simulation Analysis of Healthcare Fraud Supervision under the Government Reward and Punishment Mechanism

Abstract

This study aims to provide useful insights for the Chinese government in dealing with healthcare fraud by creating an evolutionary game model that involves hospitals, third-party entities, and the government based on the government reward and punishment mechanism. This paper analyzes the evolutionary stability of each participant's strategy choice, discusses the influence of each element on the tripartite strategy choice, and further analyzes the stability of the equilibrium point in the tripartite game system. The results show that (1) the government increasing fines on hospitals is conducive to compliant hospital operations, and the incentive mechanism has little effect on such operations; (2) the lack of an incentive mechanism for third parties results in false investigations by third parties; and (3) rewards from higher levels of government promote strict supervision by local governments, but that the high cost of supervision and rewards for hospitals inhibits the probability of strict supervision. Finally, Matlab 2020a is used for simulation analysis to provide a reference for the government to improve the supervision of healthcare fraud.

Keywords: China; Matlab; evolutionary game; reward and punishment mechanism; simulation analysis; supervision of healthcare fraud.

Figure 1

The relationship of game players.

Figure 2

Replicated dynamic phase diagram of the hospital.

Figure 3

Replicated dynamic phase diagram of the third party.

Figure 4

Replicated dynamic phase diagram of the local government.

Figure 5

Evolutionary process of the behavior of the hospital, the third party, and the local government under scenario 1: (a) the hospital, (b) the third party, and (c) the local government.

Figure 6

The effects of $C_{h1}-C_{h2}$ on evolutionary process of the behavior of hospital, the third party, and local government under scenario 1: (a) the hospital, (b) the third party, and (c) the local government.

Figure 7

The effects of $I_{h2}-I_{h1}$ on evolutionary process of the behavior of hospital, the third party, and local government under scenario 1: (a) the hospital, (b) the third party, and (c) the local government.

Figure 8

The effects of $C_g$ on evolutionary process of the behavior of hospital, the third party, and the local government under scenario 1: (a) the hospital, (b) the third party, and (c) the local government.

Figure 9

Evolutionary process of the behavior of hospital, the third party, and the local government under scenario 2: (a) the hospital, (b) the third party, and (c) the local government.

Figure 10

The effects of $C_g$ on evolutionary process of the behavior of hospital, the third party, and the local government under scenario 2: (a) the hospital, (b) the third party, and (c) the local government.

Figure 11

The effects of $A_g$ on evolutionary process of the behavior of hospital, the third party, and local government under scenario 2: (a) the hospital, (b) the third party, and (c) the local government.

Figure 12

The effects of $P_g$ on evolutionary process of the behavior of hospital, the third party, and the local government under scenario 2: (a) the hospital, (b) the third party, and (c) the local government.

Figure 13

Evolutionary process of the behavior of hospital, the third party, and the local government under scenario 3: (a) the hospital, (b) the third party, and (c) the local government.

Figure 14

The effects of $P_h$ on evolutionary process of the behavior of hospital, the third party, and local government under scenario 3: (a) the hospital, (b) the third party, and (c) the local government.

Figure 15

The effects of $A_h$ on evolutionary process of the behavior of hospital, the third party, and local government under scenario 3: (a) the hospital, (b) the third party, and (c) the local government.