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Impact of HQA Composites on Hospital Ranking-Academy Health June 2008

Impact of HQA Composites on Hospital Ranking-Academy Health June 2008. Authors: Christine Vogeli, Romana Hasnain-Wynia, Raymond Kang, Mary Beth Landrum, and Joel S. Weissman

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Impact of HQA Composites on Hospital Ranking-Academy Health June 2008

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  1. Impact of HQA Composites on Hospital Ranking-Academy Health June 2008 Authors: Christine Vogeli, Romana Hasnain-Wynia, Raymond Kang, Mary Beth Landrum, and Joel S. Weissman The authors acknowledge the assistance of the IFQHC and the Centers for Medicare and Medicaid Services (CMS) in providing data which made this research possible. The conclusions prescribed are solely those of the author(s) and do not represent those of  IFQHC or CMS. Jointly funded by The Commonwealth Fund and the Robert Wood Johnson Foundation’s Changes in Health Care Financing and Organization (HCFO) Initiative

  2. Questions • How similar are rankings based on the Composite Scores? • Opportunity Weighted, Patient Percent • All-or-None • What Types of Hospitals Fare Better under Each Composite Score?

  3. Methods • Hospital Scores Calculated using all three composites (at least 30 cases). • Opp. Weighted • Mean of Patient Percent, All-or-None • Compared Top Performing Hospitals using Agreement and Kappa Statistics • Examined Hospital Characteristics of Top and Bottom Performers

  4. Hospitals in HQA Database (30 or more Cases) • Hospital Bed Size • 18% Large, 37% Medium • 35% Small • Ownership • 57% Not-for-Profit, 14% Private-for-Profit • 18% Public

  5. Hospitals in HQA Database (30 or more Cases) • Teaching Status • 8% Major, 13% Minor • 79% Non-Teaching • Region • 14% Northeast, 37% South • 26% Midwest, 16% West

  6. Agreement and Kappa Statistics for Top Performing Hospitals

  7. Winners and Losers • Opp. Weighted vs. Patient Percent • Small Differences in the type of Hospitals categorized as top/bottom performers (1.8% difference) • All-or-None • Hurts Major Teaching Hospitals in AMI, HF • Less likely to be top performer (-3.2%) • More likely to be a bottom performer (+9.2%)

  8. Winners and Losers • All-or-None (cont.) • Hurts Large Hospitals in AMI, HF • More likely to be bottom performer (+4.1%) • Helps Midwestern Hospitals in HF • Less likely to be in the bottom (-3%) • Hurts Western Hospitals in HF • More likely to be in the bottom (+4%)

  9. Conclusions • Opp. Weighted and Patient Percent • Similar Distributions • Similar set of Winners and Losers • All-or-None • More dispersed distribution • Small but noticeable impact on Winners and Losers

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