title: A predictive model of hospitalization risk among disabled medicaid enrollees.
abstract: OBJECTIVE
  To identify Medicaid patients, based on 1 year of administrative data, who were at high risk of admission to a hospital in the next year, and who were most likely to benefit from outreach and targeted interventions.
  
  
  METHODS
  Observational cohort study for predictive modeling.
  
  
  METHODS
  Claims, enrollment, and eligibility data for 2007 from a state Medicaid program were used to provide the independent variables for a logistic regression model to predict inpatient stays in 2008 for fully covered, continuously enrolled, disabled members. The model was developed using a 50% random sample from the state and was validated against the other 50%. Further validation was carried out by applying the parameters from the model to data from a second state's disabled Medicaid population.
  
  
  RESULTS
  The strongest predictors in the model developed from the first 50% sample were over age 65 years, inpatient stay(s) in 2007, and higher Charlson Comorbidity Index scores. The areas under the receiver operating characteristic curve for the model based on the 50% state sample and its application to the 2 other samples ranged from 0.79 to 0.81. Models developed independently for all 3 samples were as high as 0.86. The results show a consistent trend of more accurate prediction of hospitalization with increasing risk score.
  
  
  CONCLUSIONS
  This is a fairly robust method for targeting Medicaid members with a high probability of future avoidable hospitalizations for possible case management or other interventions. Comparison with a second state's Medicaid program provides additional evidence for the usefulness of the model.
authors.affiliation: Jefferson School of Population Health, 901 Walnut St, 10th Fl, Philadelphia, PA 19107, USA. john.mcana@jefferson.edu
authors.forename: John F
authors.initials: JF
authors.lastname: McAna
authors.forename: Albert G
authors.initials: AG
authors.lastname: Crawford
authors.forename: Benjamin W
authors.initials: BW
authors.lastname: Novinger
authors.forename: Jaan
authors.initials: J
authors.lastname: Sidorov
authors.forename: Franklin M
authors.initials: FM
authors.lastname: Din
authors.forename: Vittorio
authors.initials: V
authors.lastname: Maio
authors.forename: Daniel Z
authors.initials: DZ
authors.lastname: Louis
authors.forename: Neil I
authors.initials: NI
authors.lastname: Goldfarb
mesh_terms: D000368:Aged; D015331:Cohort Studies; D006233:Persons with Disabilities; D005260:Female; D005544:Forecasting; D006760:Hospitalization / Q000639:trends*; D006801:Humans; D007345:Insurance Claim Review; D016015:Logistic Models; D008297:Male; D008484:Medicaid; D008875:Middle Aged; D008962:Models, Theoretical; D018570:Risk Assessment / Q000379:methods; D014481:United States