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Related Experiment Videos

Risk adjustment using automated ambulatory pharmacy data: the RxRisk model.

Paul A Fishman1, Michael J Goodman, Mark C Hornbrook

  • 1Center for Health Studies, Group Health Cooperative, Seattle, Washington 98101, USA. fishman.p@ghc.org

Medical Care
|January 25, 2003
PubMed
Summary
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The RxRisk model, using pharmacy data, predicts healthcare costs similarly to demographic models but less accurately than diagnosis-based Hierarchical Coexisting Conditions (HCCs). RxRisk offers an alternative for medical risk analysis.

Area of Science:

  • Health Services Research
  • Medical Informatics
  • Pharmacoeconomics

Background:

  • Accurate prediction of future healthcare costs is crucial for resource allocation and financial planning in managed care organizations.
  • Existing risk adjustment models often rely on diagnosis codes (ICD-9-CM) or demographic data, which may not fully capture patient complexity.
  • Automated pharmacy data offers a potentially valuable, underutilized source for identifying chronic conditions and assessing health risk.

Purpose of the Study:

  • To develop and evaluate the RxRisk model, a novel risk assessment instrument utilizing automated ambulatory pharmacy data.
  • To compare the predictive performance of RxRisk against demographic-only models, Ambulatory Clinical Groups (ACGs), and Hierarchical Coexisting Conditions (HCCs) for future healthcare costs.
  • To assess the utility of RxRisk in forecasting healthcare resource utilization.

Related Experiment Videos

Main Methods:

  • A retrospective cohort study was conducted using health services utilization and cost data from approximately 1.5 million individuals across five US Health Maintenance Organizations (HMOs).
  • The RxRisk algorithm was developed to classify prescription drug fills into chronic disease categories for both adult and pediatric populations.
  • Model performance was evaluated by comparing the variance in prospective healthcare costs explained (R-squared) by RxRisk, ACGs, and HCCs.

Main Results:

  • Hierarchical Coexisting Conditions (HCCs) demonstrated the highest accuracy in forecasting total healthcare costs, explaining 15.4% of the variance.
  • The RxRisk model explained 8.7% of the prospective cost variance, performing similarly to the Ambulatory Clinical Groups (ACGs) model, which explained 10.2%.
  • Despite differences in overall accuracy, RxRisk, ACGs, and HCCs generated comparable cost predictions for the middle 60% of the healthcare cost distribution.

Conclusions:

  • While HCCs provide more accurate total cost forecasts, the pharmacy-based RxRisk model serves as a viable alternative to diagnosis-based risk assessment instruments.
  • RxRisk's reliance on readily available pharmacy data makes it a potentially more appropriate and accessible option for certain medical risk analysis applications.
  • The choice of risk assessment model should consider the specific application and the trade-offs between data sources and predictive accuracy.