Knowledge Spillovers of Medical Big Data Under Hierarchical Medical System and Patients’ Medical Treatment Decisions

In China, there are so many patients go to the highest-level hospitals to get medical treatment directly, it is essential to study how to guide patients to take the initiative to go to the basic medical institutions first in most cases. The information asymmetry theory and the game model are applied for rational analysis in this paper. We construct two models, a doctor-patient signaling game model to study the dynamic process of patients who seek medical treatment to hospitals that are trustworthy, and a two-level treatment game model to study how to achieve a hierarchical diagnosis and reasonable referral in the case of different types of patients (general and serious conditions), considering the knowledge spillover variables supported by big data to analyze the changes in the total utility of the medical market, hospitals, and patients. With the first model analysis, it points out that only when there are three preconditions, namely, high disguising cost, controlling the income of diagnosis and treatment, and reducing the losses caused by untrustworthy behaviors, can trust signals play a real role and achieve the coordination of the optimal market type. In addition to the second model analysis, it is found that the hierarchical medical system with optimizing the allocation of medical resources has a positive effect on the benefit of the medical system and the improvement of the total welfare of patients. It also shows that the full application of big data system can further enhance the total benefit of the medical system under this system. This paper gives management suggestions for strengthening information transmission, rational design of communication system, promotion and application of big data intelligent diagnosis, and treatment for medical management departments and medical institutions at all levels to make decisions.