Evaluating Accountability for Statewide Health Cost and Quality Outcomes: The Maryland Total Cost of Care Model
Prepared for:
U.S. Department of Health and Human Services, Center for Medicare & Medicaid Innovation
Evaluation of the Maryland Total Cost of Care Model: Implementation Report
Associated Project
Key Findings
- In 2018, before the MD TCOC Model began, there was substantial room to improve targeted outcomes, including Medicare spending.
- Hospital global budgets across all payers were the strongest incentive in the MD TCOC Model for hospitals to transform care in 2019.
- In 2019, primary care practices reported making gains in the five targeted domains: Access and continuity; Care management; Comprehensiveness and coordination; Patient and caregiver engagement; and Planned care.
- In 2019, the first performance year of the Total Cost of Care Model, Maryland reduced Total Cost of Care spending (relative to national trends) by $365 million, or $88 million more than the reduction of spending achieved in 2018, the last year of the All-Payer Model.
In 2018, the Centers for Medicare & Medicaid Services (CMS) and the state of Maryland signed an agreement establishing the Maryland Total Cost of Care (MD TCOC) Model. Through the MD TCOC Model, CMS and Maryland are creating incentives and supports for hospitals, primary care practices, and other providers to transform care so they will reduce medical spending, enhance quality of care, and improve population health throughout the state. Further, Maryland has committed to generating $2 billion in Medicare savings over eight years (2019 to 2026) and to meeting population health goals. CMS is testing this model under its authority to test innovations that hold promise for reducing Medicare spending while increasing, or at least preserving, quality of care. This is the first report for the independent evaluation of the MD TCOC Model. We use program data and Medicare claims to describe model implementation in the first two years (2019 to 2020). The findings in this report will serve as a foundation for interpreting future impact estimates, including what aspects of the model might drive them.
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