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Am J Manag Care. 2003 May;9(5):381-9.

A prediction model for targeting low-cost, high-risk members of managed care organizations.

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Division of Health Policy and Administration, Department of Epidemiology and Public Health, Yale University, New Haven, Conn 06520-8034, USA.



To describe the development and validation of a predictive model designed to identify and target HMO members who are likely to incur high costs.


Split-sample multivariate regression analysis.


We studied enrollees in a 350000-member HMO with > or = 1 claim in 1998 and 1999. The prediction model uses a combination of clinical and behavioral vaiables and 1998 and 1999 claims data. The prediction model was applied and used to rank low-cost patients (1998 cost < dollars 2000) according to their estimated probability of incurring costs > or = dollars 2000 in 1999. For prospective testing, we applied our models to data that are not available in advance. The same prediction model was applied to rank a different set of low-cost patients (1999 cost < dollars 2000) according to estimated probability of incurring costs > or = dollars 2000 in 2000. Because the predictions were used for disease management purposes, the outcomes of a randomly selected control group not intervened on for the disease management program was analyzed. The predictive accuracy of the model was tested by comparing the percentages of "targeted" vs all low-cost patients who incurred high costs in the subsequent year.


Of the low-cost, top-ranked 1998 patients, 47.8% incurred high (> or = dollars 2000) medical expenses in 1999 vs 14.2% of randomly selected patients who were low cost in 1998. Of the top-ranked 1999 patients, 39.7% incurred high costs in 2000 vs 12.2% of the randomly selected low-ranked patients.


The prediction model successfully identifies low-cost, high-risk patients who are likely to incur high costs in the next 12 months.

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