title: Application of an analytic model to early readmission rates within the Department of Veterans Affairs.
abstract: OBJECTIVE
  Adverse outcome rates are increasingly used as yardsticks for the quality of hospital care. However, the validity of many outcome studies has been undermined by the application of one outcome to all patients in large, diagnostically diverse populations, many of which lack evidence of a link between antecedent process of care and the rate of the outcome, the underlying assumption of the analysis.
  
  
  METHODS
  To address this analytic problem, the authors developed a model that improves the ability to identify quality problems because it selects diseases for which there are processes of care known to affect the outcome of interest. Thus, for these diseases, the outcome is most likely to be causally related to the antecedent care. In this study of hospital readmissions, risk-adjusted models were created for 17 disease categories with strong links between process and outcome. Using these models, we identified outlier hospitals.
  
  
  RESULTS
  The authors hypothesized that if the model improved on identifying hospitals with quality of care problems, then outlier status would not be random. That is, hospitals found to have extreme rates in one year would be more likely to have extreme rates in subsequent years, and hospitals with extreme rates in one condition would be more likely to have extreme rates in related disease categories. It was hypothesized further that the correlation of outlier status across time and across diseases would be stronger in the 17 disease categories selected by the model than in 10 comparison disease categories with weak links between process and outcome.
  
  
  CONCLUSIONS
  The findings support all these hypotheses. Although the present study shows that the model selects disease-outcome pairs where hospital outlier status is not random, the causal factors leading to outlier status could include (1) systematic unmeasured patient variation, (2) practice pattern variation that, although stable with time, is not indicative of substandard care, or (3) true quality-of-care problems. Primary data collection must be done to determine which of these three factors is most causally related to hospital outlier status.
authors.affiliation: Veterans Affairs Health Services Research and Development Field Program, Houston VA Medical Center, TX 77030, USA.
authors.forename: N P
authors.initials: NP
authors.lastname: Wray
authors.forename: N J
authors.initials: NJ
authors.lastname: Peterson
authors.forename: J
authors.initials: J
authors.lastname: Souchek
authors.forename: C M
authors.initials: CM
authors.lastname: Ashton
authors.forename: J C
authors.initials: JC
authors.lastname: Hollingsworth
mesh_terms: D006302:Health Services Research; D006787:Hospitals, Veterans / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D010043:Outcome and Process Assessment, Health Care / Q000458:organization & administration*; D016311:Outliers, DRG; D010359:Patient Readmission / Q000706:statistics & numerical data*; D010818:Practice Patterns, Physicians'; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D013997:Time Factors; D014481:United States; D014493:United States Department of Veterans Affairs