title: Measuring population health risks using inpatient diagnoses and outpatient pharmacy data.
abstract: OBJECTIVE
  To examine and evaluate models that use inpatient encounter data and outpatient pharmacy claims data to predict future health care expenditures.
  
  
  METHODS
  The study group was the privately insured under-65 population in the 1997 and 1998 MEDSTAT Market Scan (R) Research Database. Pharmacy and disease profiles, created from pharmacy claims and inpatient encounter data, respectively, were used separately and in combination to predict each individual's subsequent-year health care expenditures.
  
  
  RESULTS
  The inpatient-diagnosis model predicts well for the low-hospitalization under-65 populations, explaining 8.4 percent of future individual total cost variation. The pharmacy-based and in patient-diagnosis models perform comparably overall, with pharmacy data better able to split off a group of truly low-cost people and inpatient diagnoses better able to find a small group with extremely high future costs. The model th at uses both kinds of data performed significantly better than either model alone, with an R2 value of 11.8 percent .
  
  
  CONCLUSIONS
  Comprehensive pharmacy and inpatient diagnosis classification systems are each helpful for discriminating among people according to their expected costs. Properly organized and in combination these data are promising predictors of future costs.
authors.affiliation: DxCG Inc., Boston, MA 02111, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Zhao
authors.forename: R P
authors.initials: RP
authors.lastname: Ellis
authors.forename: A S
authors.initials: AS
authors.lastname: Ash
authors.forename: D
authors.initials: D
authors.lastname: Calabrese
authors.forename: J Z
authors.initials: JZ
authors.lastname: Ayanian
authors.forename: J P
authors.initials: JP
authors.lastname: Slaughter
authors.forename: L
authors.initials: L
authors.lastname: Weyuker
authors.forename: B
authors.initials: B
authors.lastname: Bowen
mesh_terms: D000293:Adolescent; D000328:Adult; D002648:Child; D002675:Child, Preschool; D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data; D011307:Drug Prescriptions / Q000145:classification / Q000706:statistics & numerical data; D004363:Drug Utilization / Q000706:statistics & numerical data*; D005260:Female; D005544:Forecasting / Q000379:methods; D017048:Health Care Costs / Q000639:trends*; D005102:Health Expenditures / Q000639:trends*; D006305:Health Status Indicators; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007345:Insurance Claim Review; D008297:Male; D008875:Middle Aged; D017059:Models, Econometric; D010594:Pharmacies / Q000191:economics / Q000706:statistics & numerical data; D018570:Risk Assessment / Q000379:methods*; D014481:United States