Zhongmin Li, PhD University of California, Davis School of Medicine
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1 Zhongmin Li, PhD University of California, Davis School of Medicine 1
2 Health care quality and transparency of care and outcomes AHRQ s IQI and PSIs State initiatives Multiple measures vs. a single composite measure May apply to many areas of study Education/Environment, etc. Why create a composite measure? No single indicator captures quality of care Summarizing a large number of indicators (process/outcomes) into a single measure Facilitates performance ranking More comprehensive than single indicator Easier for stakeholders to understand and use Consumers, payers, providers, etc. 2
3 Loss of information Requires weighting Hospital rankings may depend on weights Is All-or-None approach fair? Equal-weighting? CMS/Hospital Quality Incentive Demonstration (HQID) Hard to interpret May seem like a black box Not always clear what is being measured 3
4 HQID: Composed of 7 individual measures Launched in October 2003 Pay-for-Performance First 3-years ( ) Hospital N=250; across 36 states Participating hospitals raised overall quality by an average of 15.8% By 2007, HQID participants scored on average 7.48 percentage points higher than non-participants 4
5 Process Measures (4 items) Aspirin prescribed at discharge Antibiotics<1 hour prior to incision Prophylactic antibiotics selection Antibiotic discontinued <24 hours after surgery Outcome Measure (3 items) Inpatient mortality/survival index Postop hemorrhage/hematoma Postop physiologic/metabolic derangement Each individual measure is weighted equally (1/7) 5
6 6
7 PROS AND CONS: Advantages Simple Transparent Avoids subjective weighting Disadvantages Heavily weighted toward process measures Not accounted for unequal measurement scales WHAT WE WANT: Weight more on outcomes Account for small sample size and rare adverse event Standardized measures to a common scale 7
8 Data source: CCORP 2012 public report of CABG surgery outcomes Hospital N=124; Patient N=11,720 Data were audited Correlation analysis Method for constructing composite measure Discussion 8
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11 Step 1. Risk models are used to compute riskadjusted outcomes by provider Step 2. Consistent Directionality: Probability of No Mortality (Morbidity)= 1-Probability of Mortality (Morbidity) Step 3. Scores are Standardized to a Common Scale 11
12 Traditional/Standard logistic regression models New York, New Jersey, Pennsylvania, California Hierarchical logistic regression models Massachusetts, STS 12
13 y = π + i i e i π i logit( π = α + π i) = log 1 i βx i π i = exp( 1+ exp( α + βxi α β + ) x i ) y=observed outcome; i=patient; π=predicted outcome; e=random error; α=overall mean; β-coefficient; x=risk factors. 13
14 Demographics (Age/Gender/Race/BMI) Operative status Pre-operative comorbidities (Creatinine level/dialysis/diabetes/cvd/pad/cld/hypertension/hepatic failure, etc.) Cardiac (Arrhythmia type/mi timing/chf/shock) Previous intervention (Prior CABG/PCI/Valve) Hemodynamic status (EF/Left main/# of diseased vessels/mitral insufficiency) 14
15 logit(π ij )=α j + βx ij α j =α + u j Hospital effects are measured by the random intercepts α j (j=1,...j), a linear combination of a grand mean ( α ) and a deviation ( u j ) from that mean Shrinkage" or "smoothed" estimators reduce meansquared prediction error Debate in literature: What was removed: true signal or random noise? 15
16 Risk-Adj. Mortality Based on Hospital Odds Ratio Spearman ρ: Risk-Adjusted Mortality Based on Hospital O/E Ratio
17 π mmmmmmmmmmmmmmmmmm = Probability of No Mortality π ssssssssssss = Probability of No Postoperative Stroke π RRRRRRRRRRRRRRRRRRRRRR = Probability of No 30 Day Readmission π IIIIII = Proportion of Patients Receiving IMA 17
18 18
19 Standardization: Each measure is re-scaled by dividing by its standard deviation (sd) CCCCCCCCCCCCCCCCCC = 1 cc [( ππ mmmmmmmm ssss mmmmmmmm )+( ππ ssssssssssss ssss SSSSSSSSSSSS )+( ππ RRRRRRRRRR ssss RRRRRRRRRR )+( ππ IIIIII ssss IIIIII )] Where cc = ( ) ssss mmmmmmmm ssss ssssssssssss ssss RRRRRRRRRR ssss IIIIII 19
20 California Hospital Performance Ratings for Coronary Artery Bypass Graft (CABG) Surgery by Region, Region Hospital Operative Mortality Post-Operative Stroke Day Readmission Internal Mammary Artery Use Composite Score Cases (Deaths) Risk-Adjusted Rate Performance Rating # Cases (Stroke) Risk-Adjusted Rate Performance Rating # Cases (Readmission) Risk-Adjusted Rate Performance Rating # Cases (Rate) Performance Rating Score Performance Rating Statewide 11,720 (247) ,119 (358) ,553 (1,292) ,527 (96.5) Sacramento Valley & Northern California Region Enloe Medical Center - Esplanade Campus Mercy General Hospital 132 (1) 0.78 Average 281 (2) 0.76 Average 129 (19) Average 113 (97.4) (9) 2.27 Average 990 (19) 2.11 Average 400 (45) Average 419 (98.8) Mercy Medical Center - Redding 104 (2) 1.50 Average 206 (2) 1.07 Average 101 (13) Average 81 (100) Mercy San Juan Hospital 114 (0) 0.00 Average 242 (2) 1.04 Average 108 (8) 7.38 Average 99 (99.0) Top 10% Rideout Memorial Hospital 96 (4) 4.47 Average 186 (7) 4.73 Worse 88 (17) Average 84 (100) Bottom 10% Shasta Regional Medical Center 69 (2) 3.52 Average 128 (0) 0.00 Average 66 (8) Average 61 (82.0) Low
21 N 124 Mean 96.9 Std Dev 1.57 Median 97.1 IQ Range:
22 Correlation to Rescaled Composite Score Mortality Stroke Readmission IMA Use 22
23 CCCCCCCCCCCCCCCCCC = ( ππ mmmmmmmm ππ ssssssssssss ππ RRRRRRRRRR ππ IIIIII )+( )+( )+( ) RRRRRRRRRR mmmmmmmm RRRRRRRRRR SSSSSSSSSSSS RRRRRRRRRR RRRRRRRRRR RRRRRRRRRR IIIIII Where range denotes the maximum minus the minimum across hospitals 23
24 Composite Scores Median = 95.0% IQR: 94.0% to 95.6% Item-Total Correlation Estimated Composite Score 0 Mortality Morbidity Meds IMA (Fall 2007 harvest data) 24
25 Composite score is a meaningful overall measure Individual measure vs. composite measure Need both Composite measure should be standardized and rescaled with objective weighting 25
26 The following slides may be used during Q&A 26
27 Perioperative Medical Care Bundle Preop B-blocker Operative Technique IMA Usage Risk-Adjusted Mortality Measure Operative Mortality Risk-Adjusted Morbidity Bundle Stroke Discharge B-blocker Renal Failure Discharge Antilipids Reoperation Discharge ASA Sternal Infection Prolonged Ventilation 27
28 Medications all-or-none composite endpoint Proportion of patients who received ALL four medications (except where contraindicated) Morbidities any-or-none composite endpoint Proportion of patients who experienced AT LEAST ONE of the five morbidity endpoints 28
29 Proportion of successful outcomes = numerator / denominator = true probability + random error Hierarchical models estimate the true probabilities 29
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