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Indiana chronic disease management program risk stratification analysis.

Jingjin Li1, Ann M Holmes, Marc B Rosenman

  • 1Department of Medicine and Children's Health Services Research, Indiana University School of Medicine, Indianapolis, and Regenstrief Institute, Inc., Indianapolis, Indiana, USA. jingjli@hotmail.com

Medical Care
|September 17, 2005
PubMed
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A simple administrative data model effectively stratified Medicaid patients for chronic disease management. This model, using past claims and prescriptions, performed comparably to complex methods in predicting future healthcare utilization.

Area of Science:

  • Health Services Research
  • Health Informatics
  • Public Health

Background:

  • Risk stratification models are crucial for identifying patients eligible for targeted chronic disease management programs.
  • Administrative data offers a potentially cost-effective source for developing such models.

Purpose of the Study:

  • To compare the efficacy of risk stratification models derived from administrative data for patient classification in chronic disease management.
  • To evaluate the performance of a simple regression model against more complex models in predicting healthcare costs.

Main Methods:

  • A cohort of 19,548 Indiana Medicaid patients with chronic heart failure or diabetes was analyzed.
  • Predictor variables from FY 2001 administrative data (patient characteristics, prescriptions, healthcare utilization) were used to predict FY 2002 total claims.

Related Experiment Videos

  • Model performance was assessed using R-squared, weighted kappa, predictive ratios, and area under the receiver operating characteristic curve.

    • Main Results:

    • A simple 3-parameter least-squares regression model (predicting FY 2002 logged total charges using FY 2001 logged total charges, number of prescriptions, and eligibility category) demonstrated strong performance.
    • This simple model achieved an R-squared of 0.30 and classification efficiency metrics including sensitivity of 0.57, specificity of 0.90, area under the receiver operator curve of 0.80, and weighted kappa of 0.51.
    • The performance of the simple model was comparable to more complex risk stratification models.

    Conclusions:

    • A straightforward risk stratification model utilizing readily available administrative data can effectively classify Medicaid members based on predicted future healthcare utilization.
    • This approach offers a practical and efficient method for patient selection for tailored chronic disease management programs.