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

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 + β 1 RF 1 +…+ β m RF m + ε, where, –Ln(LOS) is the observed log LOS for the discharge –RF i is one if the risk factor i is present, else zero –α is the expected log LOS absent any risk factors –β i 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 + β 1 Avg(RF 1 ) +…+ β m Avg(RF m ) + ε*, where, –Avg[Ln(LOS)] is the observed average log LOS for the hospital –Avg(RF i ) is the percent of discharges with risk factor i for the hospital –α is the expected average log LOS absent any risk factors –β i 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[(Y 1 ∙Y 2 ∙…∙Y n ) 1/n ] = [Ln(Y 1 )+Ln(Y 2 )+…+Ln(Y n )]/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

Calculations and Resources

23 Calculations Actual length of stay – reported by hospitals Expected length of stay – calculated from model: –y-intercept + Σ i β i RF i, where β i = parameter estimate of risk factor i & RF i = 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,

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 Jul y

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 added in July)

26 GEOMEAN Calculator

27 Questions?