Study on the evolutionary strategy of upward patient transfer in the loose medical consortia.

Medical institutions in loose medical consortia tend to have poor cooperation due to fragmented interests. We aim to explore any issues associated with patient upward transfer in a loose medical consortium system consisting of two tertiary hospitals with both cooperative and competitive relationships. A two-sided evolutionary game model was constructed to assess the stability of equilibrium strategy combinations in the process of interaction between game players under different cost-sharing scenarios and different degrees of penalties when running patient upward transfer between super triple-A hospitals (STH) and general triple-A hospitals (GTH). We found that a hospital's stabilization strategy was related to its revenue status. When a hospital has high/low revenues, it will treat patients negatively/positively, regardless of the strategy chosen by the other hospital. When the hospital has a medium revenue, the strategy choice will be related to the delay cost, delay cost sharing coefficient, government penalty and the strategic choice of the other hospital. Delay cost-sharing coefficient is an important internal factor affecting the cooperation in a medical consortium for patient upward transfer. External interventions, such as government penalty mechanisms, can improve the cooperation between hospitals when hospitals have moderate revenue.

Regional allocation of carbon emission quotas in China under the total control target.

The allocation of provincial carbon emission quotas under total amount control is an effective way for China to achieve its carbon peak and neutrality targets. Firstly, in order to study the factors influencing China's carbon emissions, the expanded STIRPAT model was constructed; and combined with the scenario analysis method, the total of national carbon emission quota under the peak scenario was predicted. Then, the index system of regional carbon quota allocation is constructed based on the principles of equity, efficiency, feasibility, and sustainability; and the allocation weight is determined by the grey correlation analysis method. Finally, the total carbon emission quota under the peak scenario is distributed in 30 provinces of China, and the future carbon emission space is also analyzed. The results show that: (1) only under the low-carbon development scenario, can China reach the peak target by 2030, with a peak carbon of about 14,080.31 million tons; (2) under the comprehensive allocation principle, China's provincial carbon quota allocation is characterized by high levels in the west and low in the east. Among them, Shanghai and Jiangsu receive fewer quotas, while Yunnan, Guangxi, and Guizhou receive more; and (3) the future carbon emission space for the entire country is modestly surplus, with regional variations. Whereas Hainan, Yunnan, and Guangxi have surpluses, Shandong, Inner Mongolia, and Liaoning have significant deficits.

The impact of environmental regulation on enterprises' green innovation under the constraint of external financing: evidence from China's industrial firms.

In recent years, the value of green innovation in achieving high-quality development in China has been increasingly recognized. However, studies on different types of green innovation under various environmental regulations have not established a systematic framework; especially, those considering external financing constraints are lacking. This study subdivides both environmental regulation and green innovation. Specifically, environmental regulation is divided into command-and-control regulation, market-incentive regulation, and public-participation regulation. Green technology innovation is divided into cleaner production technology innovation and end-of-pipe technology innovation. Moreover, this study explores whether and how environmental regulation affects green technology innovation, and investigates the moderating effect of external financing constraints, by matching the data from China Environmental Yearbook and China Industrial Enterprise Database. The results show that both command-and-control regulation and market-incentive regulation have the U-shaped relationship with cleaner production technology innovation. Meanwhile, public-participation environmental regulation significantly and positively affects cleaner production technology innovation, whereas market-incentive regulation and public-participation regulation have the inverted U-shaped relationship with end-of-pipe technology innovation. In addition, the external financing constraints have a moderating effect on the relationship between environmental regulation and cleaner production technology innovation.

What Promotes Medical Overuse: Perspective on Evolutionary Game between Administration and Medical Institutions.

Medical overuse is the leading cause of high expenditure among healthcare systems worldwide, with the degree varying from region to region. There is increasing evidence to indicate that in China, National Healthcare Security Administration (NHSA) supervision plays the most crucial role in decreasing medical overuse. For medical overuse, traditional studies focus on empirical researches and qualitative analysis, most of which ignore how the two important participants, i.e., medical institutions and NHSA, affect the strategy of each other. To reduce the losses incurred by insufficient supervision, this study starts from bounded rationality, builds an evolutionary game model to study the relations between the NHSA and medical institutions, and reveals the dynamic evolution process of the supervision of NHSA and overuse of medical institutions. Through stable evolutionary strategy analysis, numerical simulation results, and sensitive experiments under diverse scenarios, we found that when profit gap of medical overuse is high or low, medical institution will adopt fixed strategy, which is medical overuse or appropriate medical use. Only when the profit gap is at a medium level will NHSA's choice affects medical institutions' strategy. Furthermore, NHSA's strategy is affected by the profit gap between medical use and supervision cost. Our work enriches the understanding of supervision for medical overuse and provides theoretical support for the NHSA to make decisions to reach an ideal condition, i.e., to supervise without exertion.

Big Data-Enabled Analysis of Factors Affecting Patient Waiting Time in the Nephrology Department of a Large Tertiary Hospital.

The length of waiting time has become an important indicator of the efficiency of medical services and the quality of medical care. Lengthy waiting times for patients will inevitably affect their mood and reduce satisfaction. For patients who are in urgent need of hospitalization, delayed admission often leads to exacerbation of the patient's condition and may threaten the patient's life. We gathered patients' information about outpatient visits and hospital admissions in the Nephrology Department of a large tertiary hospital in western China from January 1st, 2014, to December 31st, 2016, and we used big data-enabled analysis methods, including univariate analysis and multivariate linear regression models, to explore the factors affecting waiting time. We found that gender (P=0.048), the day of issuing the admission card (Saturday, P=0.028), the applied period for admission (P < 0.001), and the registration interval (P < 0.001) were positive influencing factors of patients' waiting time. Disease type (after kidney transplantation, P < 0.001), number of diagnoses (P=0.037), and the day of issuing the admission card (Sunday, P=0.001) were negative factors. A linear regression model built using these data performed well in the identification of factors affecting the waiting time of patients in the Nephrology Department. These results can be extended to other departments and could be valuable for improving patient satisfaction and hospital service quality by identifying the factors affecting waiting time.

Patient Mix Optimization in Admission Planning under Multitype Patients and Priority Constraints.

Hospital beds are one of the most critical medical resources. Large hospitals in China have caused bed utilization rates to exceed 100% due to long-term extra beds. To alleviate the contradiction between the supply of high-quality medical resources and the demand for hospitalization, in this paper, we address the decision of choosing a case mix for a respiratory medicine department. We aim to generate an optimal admission plan of elective patients with the stochastic length of stay and different resource consumption. We assume that we can classify elective patients according to their registration information before admission. We formulated a general integer programming model considering heterogeneous patients and introducing patient priority constraints. The mathematical model is used to generate a scientific and reasonable admission planning, determining the best admission mix for multitype patients in a period. Compared with model II that does not consider priority constraints, model I proposed in this paper is better in terms of admissions and revenue. The proposed model I can adjust the priority parameters to meet the optimal output under different goals and scenarios. The daily admission planning for each type of patient obtained by model I can be used to assist the patient admission management in large general hospitals.