title: Characteristics of the divested HCA and AMI hospitals.
abstract: The primary aim of this study was to compare and contrast the predivestiture managerial and market characteristics of the following: Divested and nondivested hospitals of Hospital Corporation of America (HCA) and American Medical International (AMI). The findings indicated that HCA hospitals with 1) lower occupancy rates, 2) less growth in revenues, 3) higher debt to total asset position, 4) fewer beds, 5) less growth in their elderly populations, and 6) less growth in their markets' per capita incomes had a higher probability of being divested into HealthTrust. The results for the AMI model were similar to those for the HCA model. AMI hospitals with 1) fewer beds, 2) less growth in their markets' per capita incomes, 3) lower salary expenses per discharge, 4) lower occupancy rates, and 5) increased growth in populations had a higher probability of being divested into EPIC.
authors.affiliation: Virginia Commonwealth University, Department of Health Administration, Richmond 23298.
authors.forename: M J
authors.initials: MJ
authors.lastname: McCue
authors.forename: J P
authors.initials: JP
authors.lastname: Clement
mesh_terms: D001509:Bed Occupancy / Q000706:statistics & numerical data; D005377:Financial Management, Hospital / Q000706:statistics & numerical data; D006742:Hospital Bed Capacity / Q000706:statistics & numerical data; D006754:Hospital Restructuring / Q000191:economics / Q000706:statistics & numerical data*; D006777:Hospitals, Proprietary / Q000191:economics* / Q000706:statistics & numerical data; D006801:Humans; D007182:Income / Q000706:statistics & numerical data; D017059:Models, Econometric; D009096:Multi-Institutional Systems / Q000191:economics* / Q000706:statistics & numerical data; D012044:Regression Analysis; D012424:Rural Population / Q000706:statistics & numerical data; D012456:Salaries and Fringe Benefits / Q000706:statistics & numerical data; D014481:United States; D014505:Urban Population / Q000706:statistics & numerical data