How well does diagnosis-based risk-adjustment work for comparing ambulatory clinical outcomes?

This paper examines the empirical consistency of the Diagnosis Cost Groups/Hierarchical Condition Categories (DCG/HCC) risk-adjustment method for comparing 7-day mortality between hospital-based outpatient departments (HOPDs) and freestanding ambulatory surgery centers (ASCs). We used patient level data for the three most common outpatient procedures provided during the 1997-2004 period in Florida. We estimated base-line logistic regression models without any diagnosis-based risk adjustment and compared them to logistic regression models with the DCG/HCC risk-adjustment, and to conditional logit models with a matched cohort risk-adjustment approach. We also evaluated models that adjusted for primary diagnoses only, and then for all available diagnoses, to assess how the frequently absent secondary diagnoses fields in ambulatory surgical data affect risk-adjustment. We found that risk-adjustment using both diagnosis-based methods resulted in similar 7-day mortality estimates for HOPD patients in comparison with ASC patients in two out of three procedures. We conclude that the DCG/HCC risk-adjustment method is relatively consistent and stable, and recommend this risk-adjustment method for health policy research and practice with ambulatory surgery data. We also recommend using risk-adjustment with all available diagnoses.