title: Judging hospitals by severity-adjusted mortality rates: the case of CABG surgery.
abstract: In many health care marketplaces, outcomes assessment is central to monitoring quality while controlling costs. Comparing outcomes across providers generally requires adjustment for patient severity. For mortality rates and other adverse outcomes comparisons, severity adjustment ideally aims to control for patient characteristics prior to the health care intervention. A variety of severity methodologies, specifically for hospitalized patients, are commercially available. Some have been adopted by state or regional initiatives for publicly comparing hospital outcomes. We applied 14 common severity measures to the same data set to determine whether judgments about risk-adjusted hospital death rates are sensitive to the specific severity method. We examined 7,765 patients undergoing coronary artery bypass graft (CABG) surgery at 38 hospitals. Unadjusted death rates ranged from 0% to 11.2% across hospitals. Comparisons of relative hospital performance were relatively insensitive to the severity adjustment method.
authors.affiliation: Department of Medicine, Beth Israel Hospital, Boston, MA 02215, USA.
authors.forename: B
authors.initials: B
authors.lastname: Landon
authors.forename: L I
authors.initials: LI
authors.lastname: Iezzoni
authors.forename: A S
authors.initials: AS
authors.lastname: Ash
authors.forename: M
authors.initials: M
authors.lastname: Shwartz
authors.forename: J
authors.initials: J
authors.lastname: Daley
authors.forename: J S
authors.initials: JS
authors.lastname: Hughes
authors.forename: Y D
authors.initials: YD
authors.lastname: Mackiernan
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001026:Coronary Artery Bypass / Q000401:mortality* / Q000706:statistics & numerical data; D005260:Female; D006302:Health Services Research / Q000379:methods; D017052:Hospital Mortality; D006761:Hospitals / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010043:Outcome and Process Assessment, Health Care / Q000706:statistics & numerical data; D011379:Prognosis; D012720:Severity of Illness Index; D014481:United States / Q000453:epidemiology