Module 6
Case Studies from the Evidence
Four real Medicare models and what their federal evaluations actually found: Maryland's global budgets, mandatory joint replacement bundles, the Pioneer ACOs, and CPC+.
By the end of this module, you will be able to:
- Explain what Maryland's global budgets and mandatory joint replacement bundles achieved
- Describe why the Pioneer ACO record reads as both a success and a caution
- Explain why CPC+ improved utilization without producing net savings
- Maryland's Hospital Global Budgets The clearest American test of global budgets: what Maryland's All-Payer Model changed, and what the federal evaluation found. About 5 min
- Mandatory Joint Replacement Bundles: CJR The Comprehensive Care for Joint Replacement model tested bundled payment at scale, with participation required rather than voluntary. This case covers what eight years showed. About 5 min
- The Pioneer ACO Experiment The first accountable care model certified for expansion, and the attrition problem that came with it. This case covers both halves of the record. About 5 min
- CPC+: When a Good Model Does Not Save Money The largest primary care transformation model in Medicare history improved some utilization and still produced no net savings. This case covers why the null result is instructive. About 5 min
Module quiz
Answer all questions to see your score.
1. Across these four cases, which design feature is most consistently associated with real savings?
Maryland's budgets, CJR's episode targets, and Pioneer's two-sided risk all produced measured savings. CPC+, which enhanced payment without total-cost accountability, did not.
2. Why was CJR's mandatory participation important for the evidence base?
Voluntary models attract organizations that expect to succeed. Requiring participation in selected metro areas made CJR the closest thing to an unbiased at-scale test of bundles.
3. What did the Pioneer ACO experience demonstrate about provider risk tolerance?
Thirteen of thirty-two participants exited within two years despite being selected for their sophistication, which is why later programs built risk ladders instead of deep ends.