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Health Serv Res. 2010 Apr;45(2):532-52. doi: 10.1111/j.1475-6773.2009.01080.x. Epub 2010 Jan 27.

Using information on clinical conditions to predict high-cost patients.

Author information

1
Center for Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality, 540 Gaither Road, Rockville, MD 20850, USA. john.fleishman@ahrq.hhs.gov

Abstract

OBJECTIVE:

To compare the ability of different models to predict prospectively whether someone will incur high medical expenditures.

DATA SOURCE:

Using nationally representative data from the Medical Expenditure Panel Survey (MEPS), prediction models were developed using cohorts initiated in 1996-1999 (N=52,918), and validated using cohorts initiated in 2000-2003 (N=61,155).

STUDY DESIGN:

We estimated logistic regression models to predict being in the upper expenditure decile in Year 2 of a cohort, based on data from Year 1. We compared a summary risk score based on diagnostic cost group (DCG) prospective risk scores to a count of chronic conditions and indicators for 10 specific high-prevalence chronic conditions. We examined whether self-rated health and functional limitations enhanced prediction, controlling for clinical conditions. Models were evaluated using the Bayesian information criterion and the c-statistic.

PRINCIPAL FINDINGS:

Medical condition information substantially improved prediction of high expenditures beyond gender and age, with the DCG risk score providing the greatest improvement in prediction. The count of chronic conditions, self-reported health status, and functional limitations were significantly associated with future high expenditures, controlling for DCG score. A model including these variables had good discrimination (c=0.836).

CONCLUSIONS:

The number of chronic conditions merits consideration in future efforts to develop expenditure prediction models. While significant, self-rated health and indicators of functioning improved prediction only slightly.

PMID:
20132341
PMCID:
PMC2838159
DOI:
10.1111/j.1475-6773.2009.01080.x
[Indexed for MEDLINE]
Free PMC Article

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