title: The best of both worlds? Potential of hybrid prospective/concurrent risk adjustment.
abstract: BACKGROUND
  There remains considerable uncertainty about whether prospective or concurrent risk adjustment (RA) is preferable. Although concurrent models have better predictive power than prospective models, the large payments associated with concurrent RA create incentives for fraudulent coding. A hybrid strategy--in which prospective payments were used for patients with low expected costs and concurrent payments were available upon the diagnosis of a small number of common, expensive conditions--might improve predictive performance while requiring less auditing than fully concurrent RA. In addition, within-condition RA (using clinical data) for the selected conditions could further improve predictive power.
  
  
  OBJECTIVE
  To assess how such a hybrid strategy might perform, focusing on a small number of chronic, expensive conditions that are verifiable (hence auditable).
  
  
  METHODS
  All patients from seven health plans who had two complete years of utilization data were considered. RA models were estimated among patients younger than 65 (n = 319,209) using the Hierarchical Coexisting Conditions (HCC) model with and without stratification of the sample based on the presence of one or more of 100 verifiable, expensive, predictive conditions (VEP100). R2 and predictive ratios were calculated for each model studied.
  
  
  RESULTS
  Patients with a VEP100 condition (9.3% of the population) accounted for 84.3% of the variation in cost. R2 was 0.08 using a prospective HCC model on the entire population, but increased to 0.26 for a hybrid using prospective HCCs on the 90.7% of the sample without a VEP100 condition and a simple concurrent model consisting of dummy variables for each of the VEP100 conditions.
  
  
  CONCLUSIONS
  Combined with targeted auditing, a hybrid approach to RA could improve our ability to match payments to costs. However, because this would require additional, costly data collection, more research is needed to determine whether this benefit justifies the data collection and auditing burden.
authors.affiliation: Department of Medicine and Institute for Health Policy Studies, University of California, San Francisco, 94118, USA. adudley@itsa.ucsf.edu
authors.forename: R Adams
authors.initials: RA
authors.lastname: Dudley
authors.forename: Carol A
authors.initials: CA
authors.lastname: Medlin
authors.forename: Lisa B
authors.initials: LB
authors.lastname: Hammann
authors.forename: Miriam G
authors.initials: MG
authors.lastname: Cisternas
authors.forename: Richard
authors.initials: R
authors.lastname: Brand
authors.forename: Deborah J
authors.initials: DJ
authors.lastname: Rennie
authors.forename: Harold S
authors.initials: HS
authors.lastname: Luft
mesh_terms: D000293:Adolescent; D000328:Adult; D002908:Chronic Disease; D003625:Data Collection; D003953:Diagnosis-Related Groups; D018588:Fee-for-Service Plans; D005260:Female; D005544:Forecasting; D017048:Health Care Costs; D005102:Health Expenditures; D006279:Health Maintenance Organizations; D006801:Humans; D008297:Male; D008329:Managed Care Programs; D008485:Medical Audit; D006278:Medicare; D008875:Middle Aged; D017059:Models, Econometric; D011446:Prospective Studies; D020379:Risk Adjustment; D018570:Risk Assessment