1. 1 A cost-effectiveness framework for profiling hospital efficiency Justin Timbie AcademyHealth Annual Research Meeting June 5, 2007 Walt Disney World “Dolphin”

2. 2 Acknowledgements Sharon-Lise Normand 1,2 Joe Newhouse 1 Meredith Rosenthal 3 1 Department of Health Care Policy, Harvard Medical School 2 Department of Biostatistics, Harvard School of Public Health 3 Department of Health Policy & Management, Harvard School of Public Health

3. 3 Context Interest in efficiency measurement following growth of P4P. –42% of commercial HMOs use cost information (Rosenthal, 2005) DRA of 2005 requires Medicare to implement value based purchasing for hospital services by FY’09. –Efficiency measures to be included in FY’10-11. Measuring appropriateness and efficiency are both challenges.

4. 4 Examples of efficiency metrics Dartmouth Atlas: population-based efficiency: –Medicare spending (last 2 years of life) –Resource inputs: beds, physician FTE inputs –Utilization: hospital/ICU days, physician visits Leapfrog Group: risk-adjusted LOS, readmission rates within 14 days. National Quality Forum: focusing on LOS and readmission. Medicare: MEDPAC considering publicly reporting hospital readmission rates.

5. 5 Measurement challenges Defining efficiency: Focus on payment or resource use (LOS, readmission rates, RVUs). –DRG-based payment makes hospital efficiency profiling different. –Limited ability to measure inpatient resource use. Duration of efficiency, quality measurement. –Longer duration is desired. –Causes attribution difficulties (PAC providers). Weighting of cost vs. quality. –Binary (threshold) scoring approaches weight domains equally. –Measuring performance continuously allows tradeoffs.

6. 6 Study design Objective: Compare efficiency of hospital care following acute myocardial infarction (AMI). Motivation: Channeling patients to high-value hospitals for specific conditions. Outcomes: In-hospital survival, hospital costs. Data source: Massachusetts all payer data. –69 hospitals (11,259 patients) in FY’03. Efficiency = Health benefit relative to cost

7. 7 Methods - Cost measurement Used total hospital charges and global cost-to- charge ratios. –Costs derived from charge data remove price variation. –Use of global cost-to-charge ratios may confound estimates due to differential markup across revenue centers. Used in-hospital outcomes, although 30-day outcomes are preferred. Lacking post-acute care costs, costs of procedures.

8. 8 Methods - Estimation Link inter-hospital transfers to create inpatient “episodes.” Estimate “predicted” outcomes. –Fit hierarchical models. –Condition on hospital-specific effect, risk factors. Estimate “expected” outcomes. –Condition on population mean effect, risk factors.

9. 9 Methods - Combining measures Incremental outcomes: ΔE i = Predicted survival i – Expected survival i ΔC i = Predicted cost i – Expected cost i Incremental Net Health Benefits (INHB): Estimate P(INHB > 0) Identify efficient hospitals using relative or absolute threshold. INHB i = ΔE i – ΔC i / where = WTP/ΔE = $5M/Life saved

10. 10 Results – Threshold Scoring Standardized Cost (dollars) Standardized Survival (%) 15,00020,000 25,000 30,000 35,000 88 90 92 94

11. 11 Results - Cost-effectiveness Standardized Cost (dollars) Standardized Survival (%) 15,00020,000 25,000 30,000 35,000 88 90 92 94

12. 12 Sensitivity of INHB estimates to Willingness to Pay Threshold (Million $/Life Saved) 0 1 2 3 4 5 P (INHB > 0) 0.0 0.2 0.4 0.6 0.8 1.0

13. 13 Summary Proposed an economic approach to measuring efficiency using a composite measure. Theoretically strong and objective weighting mechanism. Results will differ from threshold model due to ability to incorporate tradeoffs. Difficult to agree on single WTP value. –LY and QALY measures of benefit are more promising.

14. 14 Future work Longitudinal analysis. Inclusion of AMI process measures, quality of life. Developing willingness to pay values that reflect multiple outputs (benefits). Refining cost measure to include RVUs. Exploring a composite measure of hospital efficiency.