Medication, diagnostic, and cost information as predictors of high-risk patients in need of care management

Christopher B. Forrest, Klaus W. Lemke, David Bodycombe, Jonathan Weiner

Research output: Contribution to journalArticle

Abstract

Objective: To contrast the advantages and limitations of using medication, diagnostic, and cost data to prospectively identify candidates for care management programs. Methods: Risk scores from prior-cost information and a set of clinically based predictive models (PMs) derived from diagnostic and medication data sources, as well as from a combination of all 3 data sources, were assigned to a national sample of commercially insured, nonelderly adults (n = 2,259,584). Clinical relevance of risk groups and statistical performance using future costs as the outcome were contrasted across the PMs. Results: Compared with prior cost, diagnostic-and medication-based PMs identified high-risk groups with a higher burden of clinically actionable characteristics. Statistical performance was similar and in some cases better for the clinical PMs compared with prior cost.The best classification accuracy was obtained with a comprehensive model that united diagnostic, medication, and prior-cost risk factors. Conclusions: Clinically based PMs are a better choice than prior cost alone for programs that seek to identify high-risk groups of patients who are amenable to care management services.

Original languageEnglish (US)
Pages (from-to)41-48
Number of pages8
JournalAmerican Journal of Managed Care
Volume15
Issue number1
Publication statusPublished - Jan 2009

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ASJC Scopus subject areas

  • Health Policy

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