title: Tracking spending among commercially insured beneficiaries using a distributed data model.
abstract: OBJECTIVE
  To explore the feasibility of using a distributed data model for ongoing reporting of local healthcare spending, specifically to investigate the contribution of utilization and pricing to geographic variation and trends in reimbursements for commercially insured beneficiaries younger than 65 years.
  
  
  METHODS
  Retrospective descriptive analysis.
  
  
  METHODS
  Commercial claims were obtained for beneficiaries in 5 states for the years 2008 to 2010 using a distributed data model. Claims were aggregated to the hospital service area (HSA) level and healthcare utilization was quantified using a novel, National Quality Forum-endorsed measure that is independent of price and allows for the calculation of resource use across all services in standardized units. We examined trends in utilization, prices, and reimbursements over time. To examine geographic variation, we mapped resource use by HSA in the 3 states from which we had data from multiple insurers. We calculated the correlation between commercial and Medicare reimbursements and utilization. Medicare claims were obtained from the Dartmouth Atlas.
  
  
  RESULTS
  We found that much of the recent growth in reimbursements for the commercially insured from 2008 to 2010 was due to increases in prices, particularly for outpatient services. As in the Medicare population, resource use by this population varied by HSA. While overall resource use patterns in the commercially insured did not mirror those among Medicare beneficiaries, we observed a strong correlation in inpatient hospital use.
  
  
  CONCLUSIONS
  This research demonstrates the feasibility and value of public reporting of standardized area-level utilization and price data using a distributed data model to understand variation and trends in reimbursements.
authors.forename: Carrie H
authors.initials: CH
authors.lastname: Colla
authors.forename: William L
authors.initials: WL
authors.lastname: Schpero
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Gottlieb
authors.forename: Asha B
authors.initials: AB
authors.lastname: McClurg
authors.forename: Peter G
authors.initials: PG
authors.lastname: Albert
authors.forename: Nancy
authors.initials: N
authors.lastname: Baum
authors.forename: Karl
authors.initials: K
authors.lastname: Finison
authors.forename: Luisa
authors.initials: L
authors.lastname: Franzini
authors.forename: Gary
authors.initials: G
authors.lastname: Kitching
authors.forename: Sue
authors.initials: S
authors.lastname: Knudson
authors.forename: Rohan
authors.initials: R
authors.lastname: Parikh
authors.forename: Rebecca
authors.initials: R
authors.lastname: Symes
authors.forename: Elliott S
authors.initials: ES
authors.lastname: Fisher
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D005260:Female; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006801:Humans; D007345:Insurance Claim Review; D007348:Insurance, Health / Q000191:economics* / Q000706:statistics & numerical data; D008297:Male; D006278:Medicare / Q000191:economics / Q000706:statistics & numerical data; D008875:Middle Aged; D015233:Models, Statistical; D012189:Retrospective Studies; D014481:United States; D055815:Young Adult