IMPORTANCEIn October 2019, Medicare changed its skilled nursing facility (SNF) reimbursement model to the Patient Driven Payment Model (PDPM), which has modified financial incentives for SNFs that may relate to therapy use and health outcomes. OBJECTIVE To assess whether implementation of the PDPM was associated with changes in therapy utilization or health outcomes. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study used a regression discontinuity (RD) approach among Medicare fee-for-service postacute-care patients admitted to a Medicarecertified SNF following hip fracture between January 2018 and March 2020. EXPOSURES Skilled nursing facility admission after PDPM implementation. MAIN OUTCOMES AND MEASURES Main outcomes were individual and nonindividual (concurrent and group) therapy minutes per day, hospitalization within 40 days of SNF admission, SNF length of stay longer than 40 days, and discharge activities of daily living score. RESULTS The study cohort included 201 084 postacute-care patients (mean [SD] age, 83.8 [8.3] years; 143 830 women [71.5%]; 185 854 White patients [92.4%]); 147 711 were admitted pre-PDPM, and 53 373 were admitted post-PDPM. A decrease in individual therapy (RD estimate: −15.9 minutes per day; 95% CI, −16.9 to −14.6) and an increase in nonindividual therapy (RD estimate: 3.6 minutes per day; 95% CI, 3.4 to 3.8) were observed. Total therapy use in the first week following admission was about 12 minutes per day (95% CI, −13.3 to −11.3) (approximately 13%) lower for residents admitted post-PDPM vs pre-PDPM. No consistent and statistically significant discontinuity in hospital readmission (0.31 percentage point increase; 95% CI, −1.46 to 2.09), SNF length of stay (2.7 percentage point decrease in likelihood of staying longer than 40 days; 95% CI, −4.83 to −0.54), or functional score at discharge (0.04 point increase in activities of daily living score; 95% CI, −0.19 to 0.26) was observed. Nonindividual therapy minutes were reduced to nearly zero in late March 2020, likely owing to COVID-19-related restrictions on communal activities in SNFs. CONCLUSIONS AND RELEVANCEIn this cross-sectional study of SNF admission after PDPM implementation, a reduction of total therapy minutes was observed following the implementation of PDPM, even though PDPM was designed to be budget neutral. No significant changes in postacute outcomes were observed. Further study is needed to understand whether the PDPM is associated with successful discharge outcomes.

Mathematical health policy models, including microsimulation models (MSMs), are widely used to simulate complex processes and predict outcomes consistent with available data. Calibration is a method to estimate parameter values such that model predictions are similar to observed outcomes of interest. Bayesian calibration methods are popular among the available calibration techniques, given their strong theoretical basis and flexibility to incorporate prior beliefs and draw values from the posterior distribution of model parameters and hence the ability to characterize and evaluate parameter uncertainty in the model outcomes. Approximate Bayesian computation (ABC) is an approach to calibrate complex models in which the likelihood is intractable, focusing on measuring the difference between the simulated model predictions and outcomes of interest in observed data. Although ABC methods are increasingly being used, there is limited practical guidance in the medical decision-making literature on approaches to implement ABC to calibrate MSMs. In this tutorial, we describe the Bayesian calibration framework, introduce the ABC approach, and provide step-by-step guidance for implementing an ABC algorithm to calibrate MSMs, using 2 case examples based on a microsimulation model for dementia. We also provide the R code for applying these methods.

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