A Game Model for Medical Service Pricing Based on the Diagnosis Related Groups.

Background: Currently there are various issues that exist in the medical institutions in China as a result of the price-setting in DRGs, which include the fact that medical institutions tend to choose patients and that the payment standard for complex cases cannot reasonably compensate the cost. Objective: The main objective is to prevent adverse selection problems in the operations of a diagnosis-related groups (DRGs) system with the game pricing model for scientific and reasonable pricing. Methods: The study proposes an improved bargaining game model over three stages, with the government and patients forming an alliance. The first stage assumes the alliance is the price maker in the Stackelberg game to maximize social welfare. Medical institutions are a price taker and decide the level of quality of medical service to maximize their revenue. A Stackelberg equilibrium solution is obtained. The second stage assumes medical institutions dominate the Stackelberg game and set an optimal service quality for maximizing their revenues. The alliance as the price taker decides the price to maximize the social welfare. Another Stackelberg equilibrium solution is achieved. The final stage establishes a Rubinstein bargaining game model to combine the Stackelberg equilibrium solutions in the first and second stage. A new equilibrium between the alliance and medical institutions is established. Results: The results show that if the price elasticity of demand increases, the ratio of cost compensation on medical institutions will increase, and the equilibrium price will increase. The equilibrium price is associated with the coefficient of patients' quality preference. The absolute risk aversion coefficient of patients affects government compensation and total social welfare. Conclusion: In a DRGs system, considering the demand elasticity and the quality preference of patients, medical service pricing can prevent an adverse selection problem. In the future, we plan to generalize these models to DRGs pricing systems with the effects of competition of medical institutions. In addition, we suggest considering the differential compensation for general hospitals and community hospitals in a DRGs system, in order to promote the goal of hierarchical diagnosis and treatment.

Lay testing cadres and point-of-care diagnostic tests for HIV and other diseases: An essential combination in health service delivery.

Zibusiso Ndlovu and Tom Ellman discuss the potential value of task sharing in provision of testing for HIV and other infectious diseases.

A multilevel mixed-effects regression analysis of the association between hospital, community and state regulatory factors, and family income eligibility limits for free and discounted care among U.S. not-for-profit, 501(c)(3), hospitals, 2010 to 2017.

BACKGROUND: Not-for-profit hospitals are facing an uncertain financial future, especially following the COVID-19 pandemic. Nevertheless, they are legally obligated to provide free and discounted health care services to communities. This study investigates the hospital, community, and state regulatory factors and whether these factors are associated with family income eligibility levels for free and discounted care. METHODS: Data were sourced from Internal Revenue Service Form 990, several data files from the Centers for Medicare and Medicaid, demographic and community factors from the Census Bureau, supplemental files from The Hilltop Institute, Community Benefit Insight, and Kaiser Family Foundation. The study employs multilevel mixed-effects linear and ordered logit regressions to estimate the association between the hospital, community, state policies, and the hospital's family income eligibility limit for free and discounted care. RESULTS: A plurality of hospitals (49.96%) offered a medium level of family income eligibility limit (160-200% of the federal poverty level (FPL)) for free care. In comparison, about 53% (52.94%) offered a low level (0-300 of FPL) eligibility limit for discounted care. Holding all else equal, hospitals designated as critical access, safety net, those in rural areas or located in disadvantaged areas were associated with an increased probability of offering low eligibility limits for free and discounted care. Hospitals in a joint venture, located in highly concentrated markets or states with minimum community benefits requirements, were associated with an increased probability of offering high eligibility limits. CONCLUSION: State and community factors appear to be associated with the eligibility level for free and discounted care. Hospitals serving low-income or rural communities seem to offer the least relief. The federal and state policymakers might need to consider relief to these hospitals with a requirement for them to provide a specific set of minimum community benefits.