Mechanism Design of Health Care Blockchain System Token Economy: Development Study Based on Simulated Real-World Scenarios

• Published in: Journal of medical Internet research 2021-09, Vol.23 (9), p.e26802-e26802
• Main Authors: Jung, Se Young, Kim, Taehyun, Hwang, Hyung Ju, Hong, Kyungpyo
• Format: Article
• Language: English
• Subjects: Activities of daily living; Automation; Blockchain; Clinical research; Clinical trials; Clinical Trials as Topic; Companies; Computerized medical records; Corporate planning; Customers; Delivery of Health Care; Design theory; Electronic Health Records; Game theory; Health insurance; Health records; Health Records, Personal; Hospitals; Humans; Incentives; Insurance policies; Life insurance; Mathematical models; Optimization; Original Paper; Participation; Patient held medical records; Patients; Personal health; Precision medicine; Privacy; Recruitment; Recruits; Rewards; Sensitivity analysis; Token Economy; Willingness to pay
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/26802

Despite the fact that the adoption rate of electronic health records has increased dramatically among high-income nations, it is still difficult to properly disseminate personal health records. Token economy, through blockchain smart contracts, can better distribute personal health records by providing incentives to patients. However, there have been very few studies regarding the particular factors that should be considered when designing incentive mechanisms in blockchain. The aim of this paper is to provide 2 new mathematical models of token economy in real-world scenarios on health care blockchain platforms. 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. 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 part