Projects

Evaluating Accountability for Statewide Health Cost and Quality Outcomes: The Maryland Total Cost of Care Model

2019-2025

Project Overview

Objective

To determine whether the Maryland Total Cost of Care Model improves quality of care and health outcomes throughout the state while reducing Medicare costs.

Project Motivation

With the Maryland Total Cost of Care Model, the state of Maryland and the Centers for Medicare & Medicaid Services (CMS) are testing whether statewide accountability for cost and quality outcomes—combined with incentives and supports for a wide range of providers to engage in care transformation—can improve health outcomes and reduce Medicare spending. CMS contracted with Mathematica to provide a rigorous, independent assessment of the implementation of the model and its impacts on targeted outcomes.

Prepared For

U.S. Department of Health and Human Services, Center for Medicare & Medicaid Innovation

The Maryland Total Cost of Care Model is one of the first Center for Medicare and Medicaid Innovation models to hold a state fully accountable for the total cost of care for Medicare beneficiaries and for improving statewide health outcomes. Mathematica is independently evaluating the effectiveness of this model.
In 2018, the Centers for Medicare and Medicaid Services (CMS) and the state of Maryland signed an agreement that established the Maryland Total Cost of Care (MD TCOC) Model. MD TCOC builds on the Maryland All-Payer Model, which ran from 2014 to 2018 and created hospital all-payer global budgets for regulated Maryland hospitals. MD TCOC continues using hospital global budgets and extends that transformation beyond hospital walls by expanding statewide accountability for cost and quality outcomes and broadening the incentives and supports to other providers to transform care. 

The goal of the evaluation is to determine whether the MD TCOC Model meets it goals to reduce Medicare spending and improve beneficiary health. This evaluation uses a mixed-methods design to describe model design and implementation, and quasi-experimental design to estimate the impact of the model on key outcomes.

Related Staff

Greg Peterson

Greg Peterson

Senior Fellow

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Jason Rotter

Jason Rotter

Senior Researcher

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Rachel Machta

Rachel Machta

Senior Researcher

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Keith Kranker

Keith Kranker

Principal Researcher

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