Journal Article

The Impact of Hospital Size on CMS Hospital Profiling

Lin, Hung-Mo, Sharma, Vansh, Gelijns, Annetine C., Egorova, Natalia N., Moskowitz, Alan J., Sosunov, Eugene A., McCardle, Ken

2016

Overview

BACKGROUND:The Centers for Medicare & Medicaid Services (CMS) profile hospitals using a set of 30-day risk-standardized mortality and readmission rates as a basis for public reporting. These measures are affected by hospital patient volume, raising concerns about uniformity of standards applied to providers with different volumes. OBJECTIVES:To quantitatively determine whether CMS uniformly profile hospitals that have equal performance levels but different volumes. RESEARCH DESIGN:Retrospective analysis of patient-level and hospital-level data using hierarchical logistic regression models with hospital random effects. Simulation of samples including a subset of hospitals with different volumes but equal poor performance (hospital effects=+3 SD in random-effect logistic model). SUBJECTS:A total of 1,085,568 Medicare fee-for-service patients undergoing 1,494,993 heart failure admissions in 4930 hospitals between July 1, 2005 and June 30, 2008. MEASURES:CMS methodology was used to determine the rank and proportion (by volume) of hospitals reported to perform “Worse than US National Rate.” RESULTS:Percent of hospitals performing “Worse than US National Rate” was ∼40 times higher in the largest (fifth quintile by volume) compared with the smallest hospitals (first quintile). A similar gradient was seen in a cohort of 100 hospitals with simulated equal poor performance (0%, 0%, 5%, 20%, and 85% in quintiles 1 to 5) effectively leaving 78% of poor performers undetected. CONCLUSIONS:Our results illustrate the disparity of impact that the current CMS method of hospital profiling has on hospitals with higher volumes, translating into lower thresholds for detection and reporting of poor performance.

• Publisher: Lippincott Williams & Wilkins,Copyright Wolters Kluwer Health, Inc. All rights reserved,Lippincott Williams & Wilkins Ovid Technologies
• Subject: Brief Report / Centers for Medicare and Medicaid Services (U.S.) - standards / Fee-for-Service Plans - statistics & numerical data / Health Facility Size - statistics & numerical data / Heart Failure / Hospital Mortality / Hospitals / Hospitals - classification / Hospitals - standards / Humans / Logistic Models / Medicaid / Mortality / Mortality risk / Patient Readmission - statistics & numerical data / Quality Indicators, Health Care - standards / Regression analysis / Retrospective Studies / Risk Adjustment - standards / Risk assessment / Simulation / United States