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Don’t These Demonstrations Ever Work? Mixed Evidence from the Four-Year Medicare Coordinated Care Demonstration AcademyHealth Annual Conference June 9, 2008. Debbie Peikes Randy Brown Arnold Chen Jennifer Schore. Random Assignment Study Design.
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Don’t These Demonstrations Ever Work? Mixed Evidence from the Four-Year Medicare Coordinated Care DemonstrationAcademyHealth Annual ConferenceJune 9, 2008 Debbie Peikes Randy Brown Arnold Chen Jennifer Schore
Random Assignment Study Design • Impact analysis (randomized, intent-to-treat design) • Effects on Medicare service use and cost • Effects on quality of care • Patient satisfaction • Physician satisfaction • Processes of care • Outcomes • Synthesis—what works and for whom? • Implementation analysis • Detailed description of enrollment and interventions • Site visits, phone calls, program MIS data
Impacts on Hospitalizations and Costs Over the First Four Years of Operations
Roadmap • Methods to Measure Impacts • Research Sample • Impacts • Hospitalizations • Traditional Part A and B costs • Total costs (with program fees) • The Challenge
Methodology • Data: Medicare EDB and SAF for claims through June 2006 • Study patients: 18,000 enrollees from programs’ start dates in 2002 through June 2005 • Followup observed: • Maximum followup (for early enrollees): 46 to 51 months • Average: 29 months [19-36 range] • Regression-adjusted for demographics, prior service use and cost, and presence of 10 chronic conditions
Programs Enrolled High-Cost Patients • Patients were high-cost • Costs were driven by hospitalizations • Average monthly Medicare expenditures for control group patients during year 1 • 5 programs: $655 to $999 • 5 programs: $1,000 to $1,999 • 5 programs: $2,000 to $3,999 • (National average was $570)
The Punch LineCare coordination is not a panacea. Although 3 of the 15 programs appeared to be cost neutral, none reduced costs.
Small Overall Effects on Hospitalizations Overall, hospitalizations down 4.5% (p=0.02), driven by sizable differences in 4 programs • Large and statistically significant reductions in 2: • Mercy -17% (p=0.02) • Georgetown -24% (p=0.06) • Moderate but not statistically significant differences in 2: • Health Quality Partners (HQP) -14% (p=0.13) • QMed -7% (p=0.38)
Most Programs Had No Discernible Effects on Hospitalizations Rest of estimates not statistically significant: • 2 had favorable differences but small samples • 3 had unfavorable differences of +4 to +12% • 6 had differences between –3 and 3%
Impact as a % of Control Group Mean # in Medicare Total Costs Treatment Part A + B (Part A and B Savings Program Group Hospitalizations Costs vs. Fee Paid) HQP 739 -14 -14* +0.3 (-$100 vs. $102) QMed 706 -7 -11 -0.2 (-$81 vs. $81) Mercy 463 -17* -9 +11.3* (-$113 vs. $248) Georgetown 114 -24* -13 -3.7 (-$335 vs. $242) Three Programs Are Likely Cost Neutral Only 1 program had a statistically significant reduction in Part A and B costs, and none reduced total costs including fees. Impact as a % of Control Group Mean # in Total Costs Treatment Part A + B (Part A and B Savings Program Group Hospitalizations Costs vs. Fee Paid) +0.3 (-$100 vs. $102) -0.2 (-$81 vs. $81) 114 -3.7 (-$335 vs. $242) HQP 739 -14 -14* QMed 706 -7 -11 Mercy 463 -17* -9 +11.3* (-$113 vs. $248) Georgetown -24* -13 * Indicates p<0.10; Cost neutral = total costs (regular Medicare costs plus program fees) of the treatment group are statistically comparable to regular Medicare costs of the control group.
No Favorable Effects on Total Costs • Pooled total costs are 11 percent higher • Same results when we trimmed outliers • Savings didn’t emerge over time
Why Doesn’t CC Control Costs Better? An Illustration of the Funnel Effect • Best case scenario, for voluntary (opt-in) model: Average of 1 hospitalization per year r 50% theoretically preventable r 30% actually prevented = 15% of hospitalizations avoided
Context for Findings • Consistent with results from other CMS demonstrations • Much harder for population-based programs. Say only 25% engage. Cost-neutral fees: • if decrease in admits is 15%: $35 pmpm • if decrease in admits is 4.5%: $10 pmpm • Fees paid were double the average monthly Medicare payments for regular office visits ($70)
Two Main Types of Measures • Measures for Impact Estimation • Both treatments and controls • Descriptive Measures • Treatment group only • Perceptions of: • Treatment group patients • Physicians of treatment group patients
Perceptions of Treatment Group Patients • Patients Generally Liked the Programs • Support/monitoring • Service arrangement • Care coordinators’ general education skills • Adherence assistance • Same 2 or 3 Programs Tended to Be Above Average Across Measures
Perceptions of Patients’ Physicians • Physicians Generally Liked Programs • Effects on medical practice • Patient self-management • Care coordination • Physician-patient relations • Care coordinators’ clinical competence • Patients’ outcomes • Would recommend to colleagues, patients • Same 1 or 2 Programs Tended to Be Above Average Across Measures
T-C Comparisons: Process of Care Measures Receipt of: • Program services --Patient survey • Health education --Patient survey • Recommended clinical --Medicare claims services • For example, hemoglobin A1c testing
T-C Comparisons: Outcome Measures • Patient knowledge -- Survey • Patient adherence -- Survey • Unmet needs -- Survey • Functioning -- Survey • Health-related quality of life -- Survey • Satisfaction with care -- Survey • Mortality -- EDB • Potentially avoidable -- Claims hospitalizations
+ – Methodology • Multiple Measures and Demonstration Sites High Potential for Type I Errors • Sought Patterns Within or Across Programs: • Program with differences in multiple measures? • Multiple programs with differences in similar measures? • Directions of significant differences: ’s = ’s? • Magnitude of estimated effect?
Summary: Some Impacts on Process Measures • Patient awareness of Large impacts programs • Reports of receiving Large impacts education • Preventive services Scattered effects
Summary: Minimal Impacts on Outcome Measures • Self-reported adherence: 0 • Unmet needs 0 • Function 0 • Health-Related Quality of Life 0 • Patient satisfaction Scattered effects • Mortality 0 • Potentially preventable Scattered effects hospitalizations
Now What? • No Substantial, Broad Quality Impacts • Recall: Programs Could Be Cost-Saving or Cost Neutral and Improve Quality • Go back and examine quality results for potentially cost-neutral programs • HQP, QMed, Mercy (at a lower fee), Geo* * Georgetown dropped out before the demonstration ended and is not considered viable due to small enrollment
Favorable Impacts on Process Measures for the 3 Selected Programs
Favorable Impacts on Outcome and T-Only Measures for 3 Selected Programs
No Structural Distinctions * The 9 programs exclude 3 that were unable to enroll enough patients over the 4 years to be considered viable.
No Distinguishing Patient Characteristics CAD = Coronary artery disease
Programs Excel in Different Domains Note: 1 = top quintile (3 programs); 5 = bottom quintile. Shaded cells are top 2 quintiles.
So What Did We Learn? • Value of DM/care coordination still unclear: • A few programs show promise, if replicable • Some proven models weren’t tested here • No single necessary or best approach • More in-person contacts better outcomes • Best target population may be medium severity
Ongoing Work • Three programs to be extended: • HQP, QMed, Mercy (at a reduced fee) • Very different models and challenges • CMS evaluation required • Two follow-up studies under way: • Extend time frame and depth (HCFO) • Test effects of intervention changes and identify best practices (MCCPRN)
Extending Time Frame and Depth: HCFO Study Tasks • Collect detailed on-site information on the 3 cost-neutral interventions • Add data for 7/06-12/07 (up to 5 years total) • Estimate effects on readmissions • Estimate effects for key subgroups • Examine effects of contamination and critical mass
Testing Intervention Changes and Defining Best Practices: MCCPRN • Includes 8 MCCD sites • Test sites’ pre-specified hypotheses about different effects over time and subgroups • Develop consensus best practices • Design demo to test best practice model • Goal: Use existing sites as ongoing laboratory for rapid testing
For More Information • http://www.mathematica-mpr.com/health/bestprac.asp • Email: rbrown@mathematica-mpr.com