title: Assessing Hospital Performance After Percutaneous Coronary Intervention Using Big Data.
abstract: Although risk adjustment remains a cornerstone for comparing outcomes across hospitals, optimal strategies continue to evolve in the presence of many confounders. We compared conventional regression-based model to approaches particularly suited to leveraging big data.
  
  
  
  We assessed hospital all-cause 30-day excess mortality risk among 8952 adults undergoing percutaneous coronary intervention between October 1, 2011, and September 30, 2012, in 24 Massachusetts hospitals using clinical registry data linked with billing data. We compared conventional logistic regression models with augmented inverse probability weighted estimators and targeted maximum likelihood estimators to generate more efficient and unbiased estimates of hospital effects. We also compared a clinically informed and a machine-learning approach to confounder selection, using elastic net penalized regression in the latter case. Hospital excess risk estimates range from -1.4% to 2.0% across methods and confounder sets. Some hospitals were consistently classified as low or as high excess mortality outliers; others changed classification depending on the method and confounder set used. Switching from the clinically selected list of 11 confounders to a full set of 225 confounders increased the estimation uncertainty by an average of 62% across methods as measured by confidence interval length. Agreement among methods ranged from fair, with a κ statistic of 0.39 (SE: 0.16), to perfect, with a κ of 1 (SE: 0.0).
  
  
  
  Modern causal inference techniques should be more frequently adopted to leverage big data while minimizing bias in hospital performance assessments.
authors.affiliation: From the Department of Health Care Policy, Harvard Medical School, Boston, MA (J.V.S., S.-L.T.N., R.W., M.C., A.L., S.R.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.).
authors.forename: Jacob V
authors.initials: JV
authors.lastname: Spertus
authors.affiliation: From the Department of Health Care Policy, Harvard Medical School, Boston, MA (J.V.S., S.-L.T.N., R.W., M.C., A.L., S.R.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.). sharon@hcp.med.harvard.edu.
authors.forename: Sharon-Lise
authors.initials: SL
authors.lastname: T Normand
authors.affiliation: From the Department of Health Care Policy, Harvard Medical School, Boston, MA (J.V.S., S.-L.T.N., R.W., M.C., A.L., S.R.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.).
authors.forename: Robert
authors.initials: R
authors.lastname: Wolf
authors.affiliation: From the Department of Health Care Policy, Harvard Medical School, Boston, MA (J.V.S., S.-L.T.N., R.W., M.C., A.L., S.R.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.).
authors.forename: Matt
authors.initials: M
authors.lastname: Cioffi
authors.affiliation: From the Department of Health Care Policy, Harvard Medical School, Boston, MA (J.V.S., S.-L.T.N., R.W., M.C., A.L., S.R.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.).
authors.forename: Ann
authors.initials: A
authors.lastname: Lovett
authors.affiliation: From the Department of Health Care Policy, Harvard Medical School, Boston, MA (J.V.S., S.-L.T.N., R.W., M.C., A.L., S.R.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.).
authors.forename: Sherri
authors.initials: S
authors.lastname: Rose
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002423:Cause of Death; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D005260:Female; D017052:Hospital Mortality; D006761:Hospitals / Q000592:standards*; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D008404:Massachusetts; D008875:Middle Aged; D015999:Multivariate Analysis; D062645:Percutaneous Coronary Intervention / Q000009:adverse effects / Q000401:mortality / Q000592:standards*; D017064:Process Assessment, Health Care / Q000592:standards*; D057216:Propensity Score; D019984:Quality Indicators, Health Care / Q000592:standards*; D012042:Registries; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome
keywords: hospital mortality; logistic regression; percutaneous coronary intervention; propensity score