Abstract

The Centers for Medicare & Medicaid Services launched the 4-year Comprehensive Primary Care Initiative (CPC Classic) in 2012 and its 5-year successor, CPC Plus (CPC+), in 2017 to test whether improving primary care delivery in five areas-and providing practices with financial and technical support-reduced spending and improved quality. This is the first study to examine long-term effects of a primary care practice transformation model. To test whether long-term primary care transformation-the 4-year CPC Classic and the first 2 years of its successor, CPC+-reduced hospitalizations, emergency department (ED) visits, and spending over 6 years. We used a difference-in-differences analysis to compare outcomes for beneficiaries attributed to CPC Classic practices with outcomes for beneficiaries attributed to comparison practices during the year before and 6 years after CPC Classic began. The study involved 565,674 Medicare fee-for-service beneficiaries attributed to 502 CPC Classic practices and 1,165,284 beneficiaries attributed to 908 comparison practices, with similar beneficiary-, practice-, and market-level characteristics as the CPC Classic practices. The interventions required primary care practices to improve 5 care areas and supported their transformation with substantially enhanced payment, data feedback, and learning support and, for CPC+, added health information technology support. Hospitalizations (all-cause), ED visits (outpatient and total), and Medicare Part A and B expenditures. Relative to comparison practices, beneficiaries in intervention practices experienced slower growth in hospitalizations-3.1% less in year 5 and 3.5% less in year 6 (P < 0.01) and roughly 2% (P < 0.1) slower growth each year in total ED visits during years 3 through 6. Medicare Part A and B expenditures (excluding care management fees) did not change appreciably. The emergence of favorable effects on hospitalizations in years 5 and 6 suggests primary care transformation takes time to translate into lower hospitalizations. Longer tests of models are needed.

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