Establishing the Cost Effectiveness of Total Joint Replacements at CMS Five-Star Rated Hospitals versus Three-Star Rated Hospitals via Markov Simulation: Demonstrating the Feasibility of Using Cost-Effectiveness Analysis to Improve U.S. Healthcare Value Today

Abstract

As the largest payor in the nation for Total Joint Replacements (TJRs) in the nation, Centers for Medicare and Medicaid Services (CMS) pays for six out of 10 TJRs representing nearly 10% of total CMS expenditure annually (Thirukumaran, 2021). Further, TJR rates are expected to increase by 401% by 2030 adding “$50 billion in cost annually” (Singh et al, 2019 p.1134). Despite numerous TJR improvement efforts by CMS, complications rates post TJR can differ as much as three-fold between hospitals driving huge variability in cost and outcomes (CMS,2021).

Considering the above and acknowledging a different approach may be needed, this study used a Markov simulation model with probabilistic sensitivity analysis to demonstrate the feasibility of using Cost Effectiveness Analysis (CEA) as an internationally proven, yet novel approach for the U.S. to improve TJR cost and quality outcomes. After demonstrating the feasibility of building the simulation model, the model was used to test the hypothesis that CMS rated five-star hospitals’ TJR care would dominate three-star hospitals as evidenced by having the highest probability of expected net benefit on the Cost Effectiveness Acceptability Frontier (CEAF).

Using a societal perspective with a 15-year time horizon, model’s transition probabilities and cost parameters were programmed by abstracting CMS hospital level submitted covered charges and complications data. Due to the lack of CMS data available to build model utilities, these parameters were abstracted from literature.

The CEAF results supported the hypothesis. Five-star hospitals dominated as the highest expected net benefit option on the CEAF for TJR at the $100,000 willingness to pay threshold (WPT). After successfully testing the hypothesis, this study discussed how CEA as a direct-to-consumer tool could be an elegant approach to improve TJR outcomes by exercising economic demand theory via the correction of information asymmetry (Folland, 2017, p. 271).