Global budgets focused on the total cost of care found to reduce costs and improve quality

The state of Maryland saved Medicare more than $780 million, reduced preventable hospital admissions, and decreased emergency department visits in the first three years of a new payment model that offers incentives and supports to a range of providers to transform care in the state, according to Mathematica’s latest evaluation report and findings at a glance. Maryland and the Centers for Medicare & Medicaid Services partnered to test the Maryland Total Cost of Care Model combines the use of global budgets, which fix the amount of revenue a hospital will receive in an upcoming year, with incentives to reduce the cost of care provided to Medicare beneficiaries from all providers, not just care from hospitals. Maryland and the Centers for Medicare & Medicaid Services partnered to test the model.

“Global budgets and the newly added accountability for all care costs are big changes to the way hospitals are paid,” said Gregory Peterson, project director and principal researcher at Mathematica. “Our evaluation of the Total Cost of Care Model finds that this new payment model is having a strong influence on hospital outcomes in Maryland and holds promise for other states looking to control rising health care costs and improve quality.”

Through the Maryland All-Payer Model, which started in 2014, Maryland became the first state to implement all-payer global budgets for most of its hospitals. In 2019, the Total Cost of Care Model broadened incentives and supports to engage a wide range of providers beyond hospitals to improve costs and quality of care, becoming one of the first states to hold hospitals accountable for the total cost of care for Medicare beneficiaries. Over the life of the Total Cost of Care Model, the state has committed to generating $2 billion in Medicare savings (by 2026).

Mathematica’s evaluation of the Maryland Total Cost of Care Model shows that it had the following favorable effects in its first three years (2019 to 2021).

• It reduced total Medicare fee-for-service spending by 2.5 percent, leading to a $781 million reduction in total spending.
• It substantially reduced rates of all-cause acute care hospital admissions (by 16.1 percent) and moderately reduced emergency department visits (by 3.8 percent).
• It improved several quality-of-care measures, including reducing potentially preventable admissions (by 16.1 percent), reducing the likelihood of an unplanned readmission to the hospital (9.5 percent), and increasing timely follow-up after hospital discharge (2.5 percent).
• The model did not affect patients’ ratings of their personal doctor or the hospitals in which they received care, suggesting that hospitals’ efforts to improve efficiency have not come at the expense of lower patient ratings.

Although the model decreased total Medicare spending (largely from decreases in hospital spending), there was a substantial increase in non-hospital spending in 2021 that lessened the overall Medicare savings that year. Future analysis will determine whether this increase was an aberration or a new trend.

The Maryland Total Cost of Care Model’s approach to holding hospitals accountable for Medicare costs from all providers is meant to encourage them to engage in broader care improvement strategies and help transform care beyond their walls. Maryland’s approach can provide other states valuable lessons on this strategy for controlling rising health care costs while improving quality. Currently, Pennsylvania and Vermont are experimenting with variations of global budgets. Mathematica will next evaluate impacts in the fourth year of the model as well as take an in-depth look at what hospitals and providers are doing to achieve cost savings and improved outcomes. Learn more about the evaluation of this model and keep up to date on future research here.

About Mathematica: Mathematica is a research and data analytics consultancy driven by a mission to improve well-being for people and communities. We innovate at the intersection of data science, social science, and technology to translate big questions into deep insights. Collaborating closely with decision makers and changemakers, we’re reimagining the way the world collects, analyzes, and applies data to solve urgent challenges.