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Schneider Institute for Health Policy, The Heller School for Social Policy and Management, Brandeis University The Effects of the Hospital Quality Incentive.

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Presentation on theme: "Schneider Institute for Health Policy, The Heller School for Social Policy and Management, Brandeis University The Effects of the Hospital Quality Incentive."— Presentation transcript:

1 Schneider Institute for Health Policy, The Heller School for Social Policy and Management, Brandeis University The Effects of the Hospital Quality Incentive Demonstration on Medicare Patient Mortality and Cost Andrew Ryan, Doctoral Candidate

2 Acknowledgements Support Agency for Healthcare Research and Quality (AHRQ) Jewish Healthcare Foundation Dissertation Committee Stan Wallack Chris Tompkins Deborah Garnick Kit Baum

3 The Hospital Quality Incentive Demonstration (HQID) Collaboration between Premier Inc. and CMS Implemented in 4 th quarter of 2003, continues today Pays a 2% bonus on Medicare reimbursement rates to hospitals performing in the top decile of a composite quality measure Pays 1% bonus for hospitals performing in the second decile (80 th -90 th percentile) of composite measure Incentivized conditions Acute myocardial infarction (AMI) Heart failure Community-acquired pneumonia Coronary-artery bypass graft (CABG) Hip and knee replacement

4 Evidence of Effectiveness of HQID Two of the three published evaluations conclude that the HQID improved process quality (Grossbart 2006; Lindauer et al. 2007) One article concluded that the HQID did not significantly improve process or mortality quality (Glickman et al. 2007) Effect of HQID on mortality examined only by Glickman (and only for AMI) No research has studied effect of HQID on Medicare cost

5 Why might HQID impact Medicare cost? Medicare has administered prices Cost can change if: Admissions change If higher quality care results from HQID, readmissions may decrease Outlier categorizations change Quality improvement may increase resource use and costs Hospitals may attempt to recoup costs from quality improvement through increased outlier categorizations

6 Data and Methods

7 Challenges estimating the effects of the HQID Selection effect: Because the HQID is voluntary, hospital participation in the HQID is a signal of a hospital’s interest in improving its clinical quality or reputation Of the 421 hospitals asked to participate in the HQID, 266 (63%) chose to participate (Lindauer et al. 2007) Hospitals’ eligibility to participate in the HQID was based on their subscription to Premier’s Perspective database, a database used for benchmarking and quality improvement activities Confounding from other time-invariant of time-varying factors (e.g. hospital size, hospital technology) Econometric approach employs 3 estimators of the effect of the HQID in the presence of unobserved selection and confounding from observables

8 Data Panel of hospitals from Because the HQID began in the fourth quarter of 2003, the study period spans the six year period from the fourth quarter of 2000 through the third quarter of 2006 Medicare fee-for-service inpatient claims -used to identify the primary diagnoses for which beneficiaries were admitted, secondary diagnoses and type of admission for risk adjustment, cost data, and discharge status to exclude transfer patients Medicare beneficiary denominator files - used to add additional risk adjusters and to determine mortality Medicare Provider of Service files - used to identify hospital structural characteristics Only short-term, acute care hospitals are included in the analysis

9 Data continued Dependent variables Risk adjusted (RA) 30-day mortality RA 60-day cost RA Outlier categorization (day or cost) Incentivized conditions examined in study: AMI, heart failure, pneumonia and CABG Hip and knee replacement excluded because of low mortality Risk adjusted outcomes: Hospital-level observed / expected Expected outcome estimated from patient-level logit and regression models Age, gender, race Elixhauser comorbidities (Elixhauser et al. 1998) Type of admission (emergency, urgent, elective) Season of admission

10 (1)RA Outcome jkt = b 1 Year t + b 2 h j + b 3 Year t * Z jt + δ HQID jt + e jkt Where j indexes to hospitals, k indexes to clinical condition, and t indexes to year. h j is a vector of hospital fixed effects Z is a vector of hospital characteristics (number of beds, teaching status, coronary-care unit, inpatient surgery, intensive care unit, open-heart surgery facility, and condition-specific Herfindahl index (a measure of market concentration)) Characteristics in Z have limited within-hospital variation so must be interacted with year in fixed effects model Assumes effect of unobserved selection is constant over observation period Effects of hospital characteristics are allowed to vary over observation period Models estimated among all acute-care hospitals Approach #1: Fixed Effects among all hospitals

11 Approach #2: Fixed Effects among hospitals eligible for HQID Equation 1 is estimated in sample of hospitals eligible for HQID hospitals HQID Eligibility is based on subscribing to Perspective database -likely indicative of an interest in improving quality Approach may better account for time-varying confounds (including selection effects)

12 Approach #3 Difference-in-difference-in- differences (DDD) among all hospitals (2) std(RA Outcome HQID Condition jkt ) - std( RA Outcome Reference jkt )= b 1 Year t + b 2 h j + b 3 Year t * Z jt + δ HQID jt + e jkt Where std is the z transformation Dependent variable is the difference between the z- transformed RA mortality for a given condition incentivized under the HQID (e.g. AMI) and a condition not incentivized under the HQID Three differences: Between conditions incentivized in HQID and conditions not incentivized in HQID Between hospitals in the HQID and those not in the HQID Before and after the HQID began Assumes that a hospital’s unobserved interest to improve quality applies to multiple domains of its clinical care while the effects of the HQID are limited to the incentivized conditions under this program

13 Approach #3 continued Z transformation normalizes RA mortality across conditions by standardizing RA rates to mean 0 and standard deviation 1 allowing for an interpretation of differences in mortality rates that is not obscured by baseline differences Difference in log mortality also evaluated Reference condition candidates are chosen from among the AHRQ inpatient mortality indicators Criteria for selection of reference conditions Criterion #1, reference condition will have a reasonably large number of hospitals that treat patients with the condition in a sufficient volume Criterion #2, reference condition will have a positive time- varying correlation with the HQID condition. Criterion #3, reference condition will not be subject to spillover effects of the HQID

14 Reference conditions for DDD HQID conditionsSelected reference conditions AMIStroke, gastrointestinal hemorrhage, abdominal aortic aneurysm (AAA), craniotomy Heart failureStroke, gastrointestinal hemorrhage, AAA, craniotomy PneumoniaStroke, gastrointestinal hemorrhage, AAA, craniotomy CABGStroke, gastrointestinal hemorrhage

15 Standard error specification Multiple observations for hospitals over time give rise to group-level heteroskedasticity Cluster-robust standard errors are estimated Hospital-level mortality rates vary in their precision as a result of the number of cases treated Analytic weights (Gould, 1994), based on number of cases treated by a hospital in a given year, are employed

16 Results

17 Effect of HQID on 30-day mortality Fixed effects all hospitals Fixed effects HQID eligible DDD - stroke DDD - Gastrointestinal hemorrhage DDD - AAA DDD - Craniotomy = 95% confidence intervals of point estimates

18 Explanation of mortality results Hospitals that participated in the HQID: Had lower mortality than non-HQID hospitals for AMI, heart failure, pneumonia, and CABG However: HQID hospitals had lower mortality before HQID began Larger hospitals tended to improve more in , after implementation of HQID HQID hospitals tend to be larger

19 Effect of HQID on 60-day cost = 95% confidence intervals of point estimates Fixed effects all hospitals Fixed effects HQID eligible DDD - stroke DDD - Gastrointestinal hemorrhage DDD - AAA DDD - Craniotomy

20 Effect of HQID on outlier categorization = 95% confidence intervals of point estimates Fixed effects all hospitals Fixed effects HQID eligible DDD - stroke DDD - Gastrointestinal hemorrhage DDD - AAA DDD - Craniotomy

21 Unique number of hospitals in models AMIHeart failureCABGPneumonia Fixed effects all hospitals 4,0764,2931,1524,325 Fixed Effects HQID eligible DDD- Gastrointestinal hemorrhage 4,0474,2041,1514,208 DDD- Stroke 4,0424,1781,1504,188 DDD- AAA 2,1832,186-2,185 DDD- Craniotomy 1,5801,583- Note: included are acute care hospitals with at least two years of data from Note: if two hospitals merged in the observation period, the analysis treats them as three unique hospitals

22 Explanation of outlier categorization results Outlier categorization tended to fall from Effect of HQID in FE models is a decrease in the decrease, not an absolute increase

23 Conclusion Using 3 different estimators of effect of HQID: No evidence of effect of HQID on mortality No evidence of effect of HQID on cost Limited evidence of a positive relationship between HQID and outlier categorization Study is consistent with finding of Glickman et al. that HQID has not reduced mortality for AMI Value-based purchasing modeled on the HQID may not increase value for Medicare


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