title: Did HEDIS get it right? Evaluating the quality of a quality measure: adherence to β-blockers and cardiovascular outcomes after myocardial infarction.
abstract: BACKGROUND
  As an example of the process that could be used to evaluate and optimize the performance of quality measures in routine practice, we evaluated whether the Healthcare Effectiveness Data and Information Set (HEDIS) measure assessing the "persistence of β-blocker treatment after a heart attack" correlates with post-myocardial infarction (MI) outcomes and whether or not there are alternative specifications of this construct which are better predictors and/or may be more easily applied.
  
  
  METHODS
  The study included a retrospective cohort of 8672 post-MI patients 18 years old and above. We assessed the strength of the association between the different adherence measures and the composite clinical outcome using multivariable Cox models. We compared the predictive capacity of each adherence definition model to one that did not contain adherence by computing the change in C-statistics and the continuous net reclassification improvement indices (NRIs).
  
  
  RESULTS
  Adherence was associated with clinical outcome reductions, with hazard ratios ranging from 0.48 (95% CI, 0.27-0.85) to 0.81 (95% CI, 0.67-0.99). None of the adherence measures, including the HEDIS definition, significantly changed the C-statistic relative to a model that did not include adherence. However, the short-term adherence measure (having 72 d covered during the first 90 d postdischarge) showed a large change in NRI (correctly reclassifying 12% of cases and 16% of noncases; NRI: 28%; 95% CI, 22%-38%), although did not significantly differ from the change in NRI with the HEDIS measure.
  
  
  CONCLUSIONS
  We identified an adherence measure that showed a predictive ability as good as that of the HEDIS definition to measure β-blocker use after MI, halving the time of assessment required, and thus, allowing for the implementation of quality improvement interventions in a more timely manner.
authors.affiliation: *Department of Medicine, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School †Department of Epidemiology, Harvard School of Public Health, Boston, MA ‡Center for Public Health Research (CSISP-FISABIO, REDISSEC), Valencia, Spain §Aetna, Hartford, CT ∥CVS Caremark, Wooksocket, RI.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Sanfélix-Gimeno
authors.forename: Jessica M
authors.initials: JM
authors.lastname: Franklin
authors.forename: William H
authors.initials: WH
authors.lastname: Shrank
authors.forename: Maria
authors.initials: M
authors.lastname: Carlo
authors.forename: Angela Y
authors.initials: AY
authors.lastname: Tong
authors.forename: Lonny
authors.initials: L
authors.lastname: Reisman
authors.forename: Olga S
authors.initials: OS
authors.lastname: Matlin
authors.forename: Troyen A
authors.initials: TA
authors.lastname: Brennan
authors.forename: Niteesh K
authors.initials: NK
authors.lastname: Choudhry
mesh_terms: D000319:Adrenergic beta-Antagonists / Q000008:administration & dosage* / Q000627:therapeutic use; D000368:Aged; D005260:Female; D019983:Guideline Adherence / Q000706:statistics & numerical data*; D006302:Health Services Research / Q000379:methods; D006801:Humans; D007345:Insurance Claim Review; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000188:drug therapy*; D017410:Practice Guidelines as Topic; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data*; D011787:Quality of Health Care; D012189:Retrospective Studies
chemical_list: D000319:Adrenergic beta-Antagonists