title: Indiana chronic disease management program risk stratification analysis.
abstract: OBJECTIVE
  The objective of this study was to compare the ability of risk stratification models derived from administrative data to classify groups of patients for enrollment in a tailored chronic disease management program.
  
  
  METHODS
  This study included 19,548 Medicaid patients with chronic heart failure or diabetes in the Indiana Medicaid data warehouse during 2001 and 2002.
  
  
  METHODS
  To predict costs (total claims paid) in FY 2002, we considered candidate predictor variables available in FY 2001, including patient characteristics, the number and type of prescription medications, laboratory tests, pharmacy charges, and utilization of primary, specialty, inpatient, emergency department, nursing home, and home health care.
  
  
  METHODS
  We built prospective models to identify patients with different levels of expenditure. Model fit was assessed using R statistics, whereas discrimination was assessed using the weighted kappa statistic, predictive ratios, and the area under the receiver operating characteristic curve.
  
  
  RESULTS
  We found a simple least-squares regression model in which logged total charges in FY 2002 were regressed on the log of total charges in FY 2001, the number of prescriptions filled in FY 2001, and the FY 2001 eligibility category, performed as well as more complex models. This simple 3-parameter model had an R of 0.30 and, in terms in classification efficiency, had a sensitivity of 0.57, a specificity of 0.90, an area under the receiver operator curve of 0.80, and a weighted kappa statistic of 0.51.
  
  
  CONCLUSIONS
  This simple model based on readily available administrative data stratified Medicaid members according to predicted future utilization as well as more complicated models.
authors.affiliation: Department of Medicine and Children's Health Services Research, Indiana University School of Medicine, Indianapolis, and Regenstrief Institute, Inc., Indianapolis, Indiana, USA. jingjli@hotmail.com
authors.forename: Jingjin
authors.initials: J
authors.lastname: Li
authors.forename: Ann M
authors.initials: AM
authors.lastname: Holmes
authors.forename: Marc B
authors.initials: MB
authors.lastname: Rosenman
authors.forename: Barry P
authors.initials: BP
authors.lastname: Katz
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Downs
authors.forename: Michael D
authors.initials: MD
authors.lastname: Murray
authors.forename: Ronald T
authors.initials: RT
authors.lastname: Ackermann
authors.forename: Thomas S
authors.initials: TS
authors.lastname: Inui
mesh_terms: D000328:Adult; D000368:Aged; D002908:Chronic Disease / Q000191:economics; D003920:Diabetes Mellitus / Q000145:classification* / Q000191:economics / Q000453:epidemiology; D019468:Disease Management; D006333:Heart Failure / Q000145:classification* / Q000191:economics / Q000453:epidemiology; D006801:Humans; D007196:Indiana / Q000453:epidemiology; D008484:Medicaid / Q000458:organization & administration* / Q000706:statistics & numerical data; D008875:Middle Aged; D017059:Models, Econometric; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods*; D013221:State Health Plans / Q000458:organization & administration / Q000706:statistics & numerical data; D014481:United States