Leapfrog’s Resource Utilization Measures & Severity-Adjustment Models

1. Leapfrog’s Resource Utilization Measures & Severity-Adjustment Models April 25, 2008

2. Townhall Call Overview • Introductions • Resource Utilization Measures • How Leapfrog is measuring resource utilization • Applicable procedures and conditions • Hospital reporting requirements • Relationship to Efficiency of Care Scores • Severity-Adjustment Models for Resource Utilization Measures • Calculations & Data Provided Back to Hospitals • Website Resources • Q & A

3. Resource Utilization Measures • Measure: Severity-adjusted average length of stay inflated by readmission rate • Length of stay associated with resource utilization • Readmission used as inflator to avoid “perverse incentive” (inappropriately releasing patients too early) • Measurement is specific to a condition--added to complement quality measures for four procedures/ conditions: CABG, PCI, AMI, and Pneumonia

4. Resource Utilization Reporting Requirements • For each procedure/condition, hospitals are asked to report: • the average length of stay (logarithmically transformed—GEOMEAN), • the number of cases followed by any readmission to that hospital within 14 days for any cause, • a count of cases with certain risk factors present • The clinical information (risk factors) and LOS/Readmission statistics needed to report these data can be accessed from the hospital’s administrative data system; no chart abstraction is necessary

5. Relationship to Efficiency of Care Scores • For each of the four procedures/conditions, the quality score and resource utilization score will be combined to create a efficiency of care score (see scoring algorithm for details) • Leapfrog will report on its website the efficiency of care scores, with a drilldown of quality and resource utilization scores

6. Severity-Adjustment Models

7. Objectives • Construct length of stay severity-adjustment model • Steps • Obtain representative national hospital discharge dataset • Review and propose candidate risk factors • Fit model for four classes of discharges • Document results in white paper

8. Data Source for Models • National Hospital Discharge Survey (NHDS) • 2003, 2004 and 2005 • De-identified, ongoing public data • 1 million total discharges • 20,000 each for AMI & PCI • 8,000 for CABG • 33,000 for pneumonia

9. Methodology • Literature review of prior modeling risk factors • SAS coding of discharge groupings and candidate risk factors based on diagnostic and procedure codes • Exploratory analysis of LOS and linear regression modeling of Ln(LOS) • Demonstrate risk adjustment impact

10. Literature Review of Risk Factors • Specifications for preliminary risk factors for each discharge group were provided by Leapfrog • Literature review supported 50 of 67 risk factors and suggested using six for other discharge groups • All preliminary and suggested risk factors were included as candidates in modeling

11. Exploratory Analysis – LOS Histogram

12. Exploratory Analysis – Means & Medians • “Heavy Tail” outlier impact, especially for small facilities • Transform LOS by taking logarithm, Ln(LOS)

13. Model Format • Ln(LOS)] = α + β1RF1 +…+ βmRFm + ε, where, • Ln(LOS) is the observed log LOS for the discharge • RFi is one if the risk factor i is present, else zero • α is the expected log LOS absent any risk factors • βi is the effect of risk factor i on the expected log LOS • ε is the “error” term for the model • Estimate parameters (α and β’s) from individual discharges available from NHDS

14. Application of Model at Hospital Level • Linear form of the model provides similar format at hospital level: • Avg[Ln(LOS)] = α + β1 Avg(RF1) +…+ βm Avg(RFm) + ε*, where, • Avg[Ln(LOS)] is the observed average log LOS for the hospital • Avg(RFi) is the percent of discharges with risk factor i for the hospital • α is the expected average log LOS absent any risk factors • βi is the effect of risk factor i on the expected average log LOS • ε* is the “error” term for the facility-level model • Ln[Geometric Mean(LOS)] = Avg[Ln(LOS)], that is, Ln[(Y1∙Y2 ∙…∙Yn)1/n] = [Ln(Y1)+Ln(Y2)+…+Ln(Yn)]/n

15. Results – AMI

16. Results – PCI

17. Results – CABG

18. Results – Pneumonia

19. Severity Adjustment LOS Impact – AMI

20. Severity Adjustment Ranking Impact – AMI

21. Recommendations • Initially employ the basic linear models • Update the model coefficients each year using the most recent three years of NHDS data • Additional recommendations can be found in the white paper

22. Calculations and Resources

23. Calculations • Actual length of stay – reported by hospitals • Expected length of stay – calculated from model: • y-intercept + ΣiβiRFi, where βi = parameter estimate of risk factor i & RFi = proportion of cases with risk factor i • Antilog of expected length of stay then taken • Standardized length of stay – (Actual LOS /expected LOS) x all-hospital average expected LOS • Standardized length of stay is then inflated by the readmission rate (1+readmission rate) • Overall score based on quartile ranking,cutpoints @6/30/2008

24. Data Provided Back to Hospitals • The following data points will be provided back to hospitals • Actual length of stay • Expected length of stay • Standardized length of stay • Readmission rate • Overall standardized length of stay, inflated by the readmission rate • How data will be provided to hospitals still a work-in-progress • Detailed scoring document available to hospitals in early July

25. Website Resources for Resource Utilization and Severity-Adjustment Models To assist hospitals in completing and understanding the resource utilization measures and the severity-adjustment models, Leapfrog makes the following tools available on the survey website: • Resource Measure Specifications • Fact Sheet on Efficiency of Care & Resource Utilization • White Paper on severity-adjustment for LOS • Automated worksheet to calculate “GEOMEAN for LOS” (see next slide) • Scoring Algorithms (more scoring details addedin July)

26. GEOMEAN Calculator

27. Questions?