title: Analyzing hospital mortality. The consequences of diversity in patient mix.
abstract: Consumers and payers increasingly demand data with which to evaluate health care providers. While publication of risk-adjusted hospital-specific death rates is one response, debate continues over whether higher than predicted mortality is a warning about quality of care or rather a reflection of a hospital's atypical patient population. To help inform this debate, we compared the characteristics of Medicare patients discharged from 187 hospitals that the Health Care Financing Administration (HCFA) had labeled "high-mortality outliers" with those of Medicare patients from 5373 hospitals not so designated. Hospitals were most likely to be flagged as high-mortality outliers by HCFA when they had large shares of very elderly patients (age greater than or equal to 85 years), patients with high-risk diagnoses, or patients requiring nursing home care. After adjustments were made to compensate for these biases, nearly half the hospitals flagged as outliers by HCFA were no longer so designated. Statistics purporting to measure effectiveness of care from hospital death rates should be modified to account for diversity in patient mix.
authors.affiliation: Department of Health Policy Research, University Medical Center, New York, NY 10016.
authors.forename: J
authors.initials: J
authors.lastname: Green
authors.forename: L J
authors.initials: LJ
authors.lastname: Passman
authors.forename: N
authors.initials: N
authors.lastname: Wintfeld
mesh_terms: D000368:Aged; D000573:American Hospital Association; D015982:Bias; D003625:Data Collection; D016208:Databases, Factual; D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data*; D006701:Home Nursing; D006761:Hospitals / Q000145:classification / Q000706:statistics & numerical data*; D006801:Humans; D006278:Medicare; D009026:Mortality; D016311:Outliers, DRG / Q000706:statistics & numerical data*; D012044:Regression Analysis; D013727:Terminal Care; D014481:United States / Q000453:epidemiology