@文章{info:doi/10.2196/26802，作者=“Jung, Se Young and Kim, Taehyun and Hwang, Hyung Ju and Hong, Kyungpyo”，标题=“医疗区块链系统Token经济机制设计:基于模拟现实场景的开发研究”，期刊=“J medical Internet Res”，年=“2021”，月=“Sep”，日=“13”，卷=“23”，数=“9”，页=“e26802”，关键词=“机制设计;优化;区块链;令牌经济;电子健康;电子健康记录;医疗保健;经济;背景:尽管电子健康记录在高收入国家的使用率显著提高，但个人健康记录的适当传播仍然困难。代币经济通过区块链智能合约，可以通过向患者提供激励，更好地分发个人健康记录。 However, there have been very few studies regarding the particular factors that should be considered when designing incentive mechanisms in blockchain. Objective: The aim of this paper is to provide 2 new mathematical models of token economy in real-world scenarios on health care blockchain platforms. Methods: First, roles were set for the health care blockchain platform and its token flow. Second, 2 scenarios were introduced: collecting life-log data for an incentive program at a life insurance company to motivate customers to exercise more and recruiting participants for clinical trials of anticancer drugs. In our 2 scenarios, we assumed that there were 3 stakeholders: participants, data recipients (companies), and data providers (health care organizations). We also assumed that the incentives are initially paid out to participants by data recipients, who are focused on minimizing economic and time costs by adapting mechanism design. This concept can be seen as a part of game theory, since the willingness-to-pay of data recipients is important in maintaining the blockchain token economy. In both scenarios, the recruiting company can change the expected recruitment time and number of participants. Suppose a company considers the recruitment time to be more important than the number of participants and rewards. In that case, the company can increase the time weight and adjust cost. When the reward parameter is fixed, the corresponding expected recruitment time can be obtained. Among the reward and time pairs, the pair that minimizes the company's cost was chosen. Finally, the optimized results were compared with the simulations and analyzed accordingly. Results: To minimize the company's costs, reward--time pairs were first collected. It was observed that the expected recruitment time decreased as rewards grew, while the rewards decreased as time cost grew. Therefore, the cost was represented by a convex curve, which made it possible to obtain a minimum---an optimal point---for both scenarios. Through sensitivity analysis, we observed that, as the time weight increased, the optimized reward increased, while the optimized time decreased. Moreover, as the number of participants increased, the optimization reward and time also increased. Conclusions: In this study, we were able to model the incentive mechanism of blockchain based on a mechanism design that recruits participants through a health care blockchain platform. This study presents a basic approach to incentive modeling in personal health records, demonstrating how health care organizations and funding companies can motivate one another to join the platform. ", issn="1438-8871", doi="10.2196/26802", url="//www.mybigtv.com/2021/9/e26802", url="https://doi.org/10.2196/26802", url="http://www.ncbi.nlm.nih.gov/pubmed/34515640" }