Identifying Future High-Healthcare Users

摘要

Objective: Diagnosis-based risk-adjustment measures are increasingly being promoted as disease managementtools. We compared the ability of several types of predictive models to identify future high-risk older peoplelikely to benefit from disease management.Study design: Veterans Health Administration (VHA) data were used to identify veterans >=65 years of age whoused healthcare services during fiscal years (FY) 1997 and 1998 and who remained alive through FY 1997. Thisyielded a development sample of 412 679 individuals and a validation sample of 207 294.Methods: Prospective risk-adjustment models were fitted and tested using Adjusted Clinical Groups (ACGs),Diagnostic Cost Groups (DCGs), a prior-utilization model (prior), and combined models (prior + ACGs andprior + DCGs). Prespecified high use in FY 1998 was defined as >=92 days of care (top 2.2%) for an individual(i.e. the number of days during the year in which an individual received inpatient or outpatient healthcareservices). We developed a second outcome, defined as >=164 days of care (top 1.0%), to explore whetherchanging the criterion for high risk would affect the number of misclassifications.Results: The diagnosis-based models performed better than the prior model in identifying a subgroup of futurehigh-cost individuals with high disease burden and chronic diseases appropriate for disease management. Thecombined models performed best at correctly classifying those without high use in the prospective year. Theutility for efficiently identifying high-risk cases appeared limited because of the high number of individualsmisclassified as future high-risk cases by all the models. Changing the criterion for high risk generally decreasedthe number of patients misclassified. There was little agreement between the models regarding who wasidentified as high risk.Conclusion: Health plans should be aware that different risk-adjustment measures may select dissimilar groupsof individuals for disease management. Although diagnosis-based measures show potential as predictivemodeling tools, combining a diagnosis-based measure with prior-utilization model may yield the best results.