Case-mix groups for VA hospital-based home care

Med Care. 1992 Jan;30(1):1-16. doi: 10.1097/00005650-199201000-00001.

Abstract

The purpose of this study is to group hospital-based home care (HBHC) patients homogeneously by their characteristics with respect to cost of care to develop alternative case mix methods for management and reimbursement (allocation) purposes. Six Veterans Affairs (VA) HBHC programs in Fiscal Year (FY) 1986 that maximized patient, program, and regional variation were selected, all of which agreed to participate. All HBHC patients active in each program on October 1, 1987, in addition to all new admissions through September 30, 1988 (FY88), comprised the sample of 874 unique patients. Statistical methods include the use of classification and regression trees (CART software: Statistical Software; Lafayette, CA), analysis of variance, and multiple linear regression techniques. The resulting algorithm is a three-factor model that explains 20% of the cost variance (R2 = 20%, with a cross validation R2 of 12%). Similar classifications such as the RUG-II, which is utilized for VA nursing home and intermediate care, the VA outpatient resource allocation model, and the RUG-HHC, utilized in some states for reimbursing home health care in the private sector, explained less of the cost variance and, therefore, are less adequate for VA home care resource allocation.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Aftercare / classification*
  • Aftercare / economics
  • Aftercare / statistics & numerical data
  • Aged
  • Algorithms
  • Analysis of Variance
  • Cost Allocation
  • Costs and Cost Analysis
  • Diagnosis-Related Groups / economics
  • Diagnosis-Related Groups / statistics & numerical data*
  • Female
  • Home Care Services / classification*
  • Home Care Services / economics
  • Home Care Services / statistics & numerical data
  • Hospitals, Veterans / economics
  • Hospitals, Veterans / organization & administration*
  • Humans
  • Male
  • Mathematical Computing
  • Models, Econometric*
  • Models, Statistical*
  • Prospective Studies
  • Regression Analysis
  • Retrospective Studies
  • Socioeconomic Factors
  • United States