Predicting Elderly at Risk of Increased Future Healthcare Use: How Much Does Diagnostic Information Add to Prior Utilization?

Abstract

We determined whether case-mix information from administrative data can identify those likely to be high users of healthcare in the following year. An individual's healthcare utilization equaled the number of days (between 1 and 365) during the year on which an individual received inpatient or outpatient services. A binary outcome was defined as using 92 days or more (i.e., being in the top 2%) in year two. We included case-mix data in the models from two risk adjustment systems, Adjusted Diagnostic Groups (ADGs) from Adjusted Clinical Groups and Hierarchical Condition Categories (HCCs) from Diagnostic Cost Groups. We examined three types of logistic regression models: (1) prior use models (year one utilization plus age and sex), (2) diagnostic models (HCCs and ADGs as dummy variables plus age and sex), and (3) combined models (prior use plus diagnostic models). For the models with the best c-statistics (i.e., area under the receiver operating characteristic (ROC) curve), we compared ROC curve plots. We also fit linear regression models and compared their sensitivity and specificity to the logistic models.