The use of administrative data to risk-stratify asthmatic patients

Am J Med Qual. Summer 1997;12(2):113-9. doi: 10.1177/0885713X9701200205.


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 management and increased educational efforts. Using logistic 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 additional research possibilities associated with the identification of high risk subscribers using only administrative data are outlined.

MeSH terms

  • Adult
  • Asthma / classification*
  • Case Management*
  • Female
  • Forecasting*
  • Humans
  • Logistic Models
  • Male
  • Medical Records Systems, Computerized*
  • Pennsylvania
  • Reproducibility of Results
  • Risk Factors
  • United States