title: A decision-theoretic approach to identifying future high-cost patients.
abstract: OBJECTIVE
  The objective of this study was to develop and evaluate a method of allocating funding for very-high-cost (VHC) patients among hospitals.
  
  
  METHODS
  Diagnostic cost groups (DCGs) were used for risk adjustment. The patient population consisted of 253,013 veterans who used Department of Veterans Affairs (VA) medical care services in fiscal year (FY) 2003 (October 1, 2002-September 30, 2003) in a network of 8 VA hospitals. We defined VHC as greater than 75,000 dollars (0.81%). The upper fifth percentile was also used for comparison.
  
  
  METHODS
  A Bayesian decision rule for classifying patients as VHC/not VHC using DCGs was developed and evaluated. The method uses FY 2003 DCGs to allocate VHC funds for FY 2004. We also used FY 2002 DCGs to allocate VHC funds for FY 2003 for comparison. The resulting allocation was compared with using the allocation of VHC patients among the hospitals in the previous year.
  
  
  RESULTS
  The decision rule identified DCG 17 as the optimal cutoff for identifying VHC patients for the next year. The previous year's allocation came closest to the actual distribution of VHC patients.
  
  
  CONCLUSIONS
  The decision-theoretic approach may provide insight into the economic consequences of classifying a patient as VHC or not VHC. More research is needed into methods of identifying future VHC patients so that capitation plans can fairly reimburse healthcare systems for appropriately treating these patients.
authors.affiliation: Division of Health Policy and Quality, Houston Center for Quality of Care and Utilization Studies, a Health Services Research and Development Center of Excellence, Houston VA Medical Center, Texas 77030, USA. kpietz@bcm.tmc.edu
authors.forename: Kenneth
authors.initials: K
authors.lastname: Pietz
authors.forename: Margaret M
authors.initials: MM
authors.lastname: Byrne
authors.forename: Laura A
authors.initials: LA
authors.lastname: Petersen
mesh_terms: D000368:Aged; D001499:Bayes Theorem; D003662:Decision Theory; D005260:Female; D017721:Hospital Costs / Q000706:statistics & numerical data*; D006801:Humans; D007297:Inpatients / Q000145:classification*; D008297:Male; D008875:Middle Aged; D014481:United States; D014493:United States Department of Veterans Affairs