Maryland's Experiment With Capitated Payments For Rural Hospitals: Large Reductions In Hospital-Based Care.

In 2010 Maryland replaced fee-for-service payment for some rural hospitals with "global budgets" for hospital-provided services called Total Patient Revenue (TPR). A principal goal was to incentivize hospitals to manage resources efficiently. Using a difference-in-differences design, we compared eight TPR hospitals to seven similar non-TPR Maryland hospitals to estimate how TPR affected hospital-provided services. We also compared health care use by "treated" patients in TPR counties to that of patients in counties containing control hospitals. Inpatient admissions and outpatient services fell sharply at TPR hospitals, increasingly so over the period that TPR was in effect. Emergency department (ED) admission rates declined 12 percent, direct (non-ED) admissions fell 23 percent, ambulatory surgery center visits fell 45 percent, and outpatient clinic visits and services fell 40 percent. However, for residents of TPR counties, visits to all Maryland hospitals fell by lesser amounts and Medicare spending increased, which suggests that some care moved outside of the global budget. Nonetheless, we could not assess the efficiency of these shifts with our data, and some care could have moved to more efficient locations. Our evidence suggests that capitation models require strong oversight to ensure that hospitals do not respond by shifting costs to other providers.

Bending the cost curve? Results from a comprehensive primary care payment pilot.

BACKGROUND: There is much interest in understanding how using bundled primary care payments to support a patient-centered medical home (PCMH) affects total medical costs. RESEARCH DESIGN AND SUBJECTS: We compare 2008-2010 claims and eligibility records on about 10,000 patients in practices transforming to a PCMH and receiving risk-adjusted base payments and bonuses, with similar data on approximately 200,000 patients of nontransformed practices remaining under fee-for-service reimbursement. METHODS: We estimate the treatment effect using difference-in-differences, controlling for trend, payer type, plan type, and fixed effects. We weight to account for partial-year eligibility, use propensity weights to address differences in exogenous variables between control and treatment patients, and use the Massachusetts Health Quality Project algorithm to assign patients to practices. RESULTS: Estimated treatment effects are sensitive to: control variables, propensity weighting, the algorithm used to assign patients to practices, how we address differences in health risk, and whether/how we use data from enrollees who join, leave, or change practices. Unadjusted PCMH spending reductions are 1.5% in year 1 and 1.8% in year 2. With fixed patient assignment and other adjustments, medical spending in the treatment group seems to be 5.8% (P=0.20) lower in year 1 and 8.7% (P=0.14) lower in year 2 than for propensity-weighted, continuously enrolled controls; the largest proportional 2-year reduction in spending occurs in laboratory test use (16.5%, P=0.02). CONCLUSIONS: Although estimates are imprecise because of limited data and quasi-experimental design, risk-adjusted bundled payment for primary care may have dampened spending growth in 3 practices implementing a PCMH.