title: Length of stay data as a guide to hospital economic performance for ICU patients.
abstract: BACKGROUND
  Length of stay data are increasingly used to monitor ICU economic performance. How such material is presented greatly affects its utility.
  
  
  OBJECTIVE
  To develop a weighted length of stay index and to estimate expected length of stay. To assess alternative ways to summarize weighted length of stay to evaluate ICU economic performance.
  
  
  METHODS
  Retrospective database study.
  
  
  METHODS
  Data for 751 ICU patients in 1998 at two hospitals used to develop weighted length of stay index. Data on 42,237 patients from 72 ICUs used as the basis of economic performance evaluation.
  
  
  METHODS
  Difference between actual and expected weighted length of stay, where expected weighted length of stay is based on patient clinical characteristics.
  
  
  RESULTS
  Length of stay statistically explains approximately 85 to 90% of interpatient variation in hospital costs. The first ICU day is approximately four times as expensive, and other ICU days approximately 2.5 times as expensive, as non-ICU hospital days. In a regression model for weighted length of stay, patient clinical characteristics explain 26% of variation. ICU economic performance can be measured by excess weighted length of stay of a "typical" patient or by occurrence of long excess weighted lengths of stay. Although different summary measures of performance are highly correlated, choice of measure affects relative ranking of some ICUs' performance.
  
  
  CONCLUSIONS
  Providers of statistical data on ICU economic performance should adjust length of stay for patient characteristics and provide multiple summary measures of the statistical distribution, including measures that address both the typical patient and outliers.
authors.affiliation: Department of Economics, Mount Holyoke College, South Hadley, Massachusetts 01075, USA. rapoport@mtholyoke.edu
authors.forename: John
authors.initials: J
authors.lastname: Rapoport
authors.forename: Daniel
authors.initials: D
authors.lastname: Teres
authors.forename: Yonggang
authors.initials: Y
authors.lastname: Zhao
authors.forename: Stanley
authors.initials: S
authors.lastname: Lemeshow
mesh_terms: D000066:Accounting; D019985:Benchmarking; D004469:Economics, Hospital / Q000706:statistics & numerical data*; D005375:Financial Audit / Q000379:methods*; D006302:Health Services Research; D017721:Hospital Costs / Q000706:statistics & numerical data; D006801:Humans; D033181:Information Dissemination; D007362:Intensive Care Units / Q000191:economics* / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000191:economics* / Q000706:statistics & numerical data; D008404:Massachusetts; D017059:Models, Econometric; D012044:Regression Analysis; D012189:Retrospective Studies; D014481:United States