Policy Flight Simulators: Accelerating Decisions to Adopt Evidence-Based Health Interventions

Abstract [from journal]

In this study, the authors used simulation to explore factors that might influence hospitals' decisions to adopt evidence-based interventions. Specifically, they developed a simulation model to examine the extent to which hospitals would benefit economically from the transitional care model (TCM). The TCM is designed to transition high-risk older adults from hospitals back to communities using interventions focused on preventing readmissions.The authors used qualitative methods to identify and validate simulation facets. Four simulation experiments explored the economic impact of the TCM on more than 3,000 U.S. hospitals: (1) magnitude of readmission penalty, (2) application to specific diagnosis-related groups, (3) level of cost sharing between payer and provider, and (4) capitated versus fee-for-service payments. The simulator projected hospital-specific economic effects. The authors used Monte Carlo methods for the simulations, which were parameterized with public data sets from the Centers for Medicare & Medicaid Services (CMS) and TCM data from randomized controlled trials and comparative effectiveness studies.Under current conditions, the simulation indicated that only 10 of more than 3,000 Medicare-certified hospitals would benefit financially from the TCM. If current readmission penalties were doubled, the number of hospitals projected to benefit would increase to 300. Targeting selected diagnosis cohorts would also increase the number of hospitals to 300. If payers reimbursed providers for 100% of the TCM costs, 2,000 hospitals would benefit financially. Under a capitated payment model, 1,500 hospitals would benefit from the TCM.Current CMS penalties-or reasonable increases-have little economic effect on the TCM. In the current environment, two strategies are likely to facilitate adoption: (1) persuading payers to reimburse TCM costs and (2) focusing on hospitals with higher bed occupancies and higher revenue patients.