title: Improving Hospital Performance Rankings Using Discrete Patient Diagnoses for Risk Adjustment of Outcomes.
abstract: To assess the changes in patient outcome prediction and hospital performance ranking when incorporating diagnoses as risk adjusters rather than comorbidity indices.
  
  
  
  Healthcare Cost and Utilization Project State Inpatient Databases for New York State, 2005-2009.
  
  
  
  Conducted tree-based classification for mortality and readmission by incorporating discrete patient diagnoses as predictors, comparing with traditional comorbidity indices such as those used for Centers for Medicare and Medicaid Services (CMS) outcome models.
  
  
  
  Diagnosis codes as predictors increased predictive accuracy 5.6 percent (95% CI: 4.5-6.9 percent) relative to CMS condition categories for heart failure 30-day mortality. Most other outcomes exhibited statistically significant accuracy gains and facility ranking shifts. Sensitivity analysis showed improvements even when predictors were limited to only the diagnoses included in CMS models.
  
  
  
  Discretizing patient severity information beyond the levels of traditional comorbidity indices improves patient outcome predictions and substantially shifts facility rankings.
authors.affiliation: University of Pittsburgh School of Medicine, Pittsburgh, PA.
authors.forename: Brendan
authors.initials: B
authors.lastname: DeCenso
authors.affiliation: Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA.|Division of Emergency Medicine, University of Washington, Seattle, WA.
authors.forename: Herbert C
authors.initials: HC
authors.lastname: Duber
authors.affiliation: Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA.
authors.forename: Abraham D
authors.initials: AD
authors.lastname: Flaxman
authors.affiliation: Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN.
authors.forename: Shane M
authors.initials: SM
authors.lastname: Murphy
authors.affiliation: Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA.
authors.forename: Michael
authors.initials: M
authors.lastname: Hanlon
mesh_terms: D000367:Age Factors; D019985:Benchmarking / Q000379:methods*; D014487:Centers for Medicare and Medicaid Services, U.S. / Q000706:statistics & numerical data; D015897:Comorbidity; D006333:Heart Failure / Q000401:mortality; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D010359:Patient Readmission / Q000706:statistics & numerical data; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data; D011787:Quality of Health Care / Q000706:statistics & numerical data; D020379:Risk Adjustment / Q000379:methods*; D012720:Severity of Illness Index; D012737:Sex Factors; D012959:Socioeconomic Factors; D016896:Treatment Outcome; D014481:United States
keywords: Medicare; Risk adjustment; machine learning