title: Systematic review of risk adjustment models of hospital length of stay (LOS).
abstract: BACKGROUND
  Policy decisions in health care, such as hospital performance evaluation and performance-based budgeting, require an accurate prediction of hospital length of stay (LOS). This paper provides a systematic review of risk adjustment models for hospital LOS, and focuses primarily on studies that use administrative data.
  
  
  METHODS
  MEDLINE, EMBASE, Cochrane, PubMed, and EconLit were searched for studies that tested the performance of risk adjustment models in predicting hospital LOS. We included studies that tested models developed for the general inpatient population, and excluded those that analyzed risk factors only correlated with LOS, impact analyses, or those that used disease-specific scales and indexes to predict LOS.
  
  
  RESULTS
  Our search yielded 3973 abstracts, of which 37 were included. These studies used various disease groupers and severity/morbidity indexes to predict LOS. Few models were developed specifically for explaining hospital LOS; most focused primarily on explaining resource spending and the costs associated with hospital LOS, and applied these models to hospital LOS. We found a large variation in predictive power across different LOS predictive models. The best model performance for most studies fell in the range of 0.30-0.60, approximately.
  
  
  CONCLUSIONS
  The current risk adjustment methodologies for predicting LOS are still limited in terms of models, predictors, and predictive power. One possible approach to improving the performance of LOS risk adjustment models is to include more disease-specific variables, such as disease-specific or condition-specific measures, and functional measures. For this approach, however, more comprehensive and standardized data are urgently needed. In addition, statistical methods and evaluation tools more appropriate to LOS should be tested and adopted.
authors.affiliation: *Department of Economics and Community Health Sciences †Department of Community Health Sciences and Centre for Health and Policy Studies, University of Calgary, Calgary, AB, Canada.
authors.forename: Mingshan
authors.initials: M
authors.lastname: Lu
authors.forename: Tolulope
authors.initials: T
authors.lastname: Sajobi
authors.forename: Kelsey
authors.initials: K
authors.lastname: Lucyk
authors.forename: Diane
authors.initials: D
authors.lastname: Lorenzetti
authors.forename: Hude
authors.initials: H
authors.lastname: Quan
mesh_terms: D003953:Diagnosis-Related Groups; D006305:Health Status Indicators; D017052:Hospital Mortality; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data; D011787:Quality of Health Care / Q000706:statistics & numerical data*; D020379:Risk Adjustment / Q000379:methods* / Q000706:statistics & numerical data*