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American Journal of Medical Quality, Vol. 12, No. 2, 113-119 (1997)
DOI: 10.1177/0885713X9701200205

The Use of Administrative Data to Risk-Stratify Asthmatic Patients

James Grana, Ph.D.

U.S. Quality Algorithms Inc., Blue Bell, Pennsylvania

Samuel Preston, B.A.

U.S. Quality Algorithms Inc., Blue Bell, Pennsylvania

Patricia D. McDermott, R.N.

U.S. Quality Algorithms Inc., Blue Bell, Pennsylvania

Nicholas A. Hanchak, M.D.

U.S. Quality Algorithms Inc., Blue Bell, Pennsylvania

In this article, a simple methodology to risk-stratify asthmatics is presented and validated. Such a model can be used to identify those high risk and more severely ill asthmatics who could benefit the most from case man agement and increased educational efforts. Using logis tic regression, the model was created to predict the probability of an asthma-related admission among all asthmatics who were members of a large HMO during calendar year 1994 (N = 54,573). The model used data from pharmacy, laboratory, and specialist claims, as well as encounter and demographic data available in U.S. Healthcare®'s administrative database. A member's prior asthma-specific utilization patterns, pharmaceutically determined severity of illness, and length of enrollment in the managed care organization had the most influence on the equation. A cross-validation of the model confirms how administrative data can be used to accurately risk- stratify those with a chronic disease. Finally, some ad ditional research possibilities associated with the iden tification of high risk subscribers using only administrative data are outlined.


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