APPLICATION OF AN ANALYTIC MODEL TO EARLY READMISSION RATES WITHIN THE DEPARTMENT-OF-VETERANS-AFFAIRS

OBJECTIVES. Adverse outcome rates are increasingly used as yardsticks for the quality of hospital care. However, the validity of many outcom e 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 rat e of the outcome, the underlying assumption of the analysis. METHODS. TO address this analytic problem, the authors developed a model that i mproves the ability to identify quality problems because it selects di seases for which there are processes of care known to affect the outco me of interest. Thus, for these diseases, the outcome is most likely t o be causally related to the antecedent care. In this study of hospita l readmissions, risk-adjusted models were created for 17 disease categ ories with strong links between process and outcome. Using these model s, we identified outlier hospitals. RESULTS. The authors hypothesized that if the model improved on identifying hospitals with quality of ca re problems, then outlier status would not be random. That is, hospita ls found to have extreme rates in one year would be more likely to hav e 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. CONCL USIONS. The findings support all these hypotheses. Although the presen t study shows that the model selects disease;outcome pairs where hospi tal outlier status is not random, the causal factors leading to outlie r status could include (1) systematic unmeasured patient variation, (2 ) practice pattern variation that, although stable with time, is not i ndicative of substandard care, or (3) true quality-of-care problems. P rimary data collection must be done to determine which of these three factors is most causally related to hospital outlier status.