title: Analysis of cost outliers within APR-DRGs in a Belgian general hospital: two complementary approaches.
abstract: OBJECTIVE
  The objective of this study was to find factors that could explain high and low resource use outliers, by associating an explanatory analysis with a statistical analysis.
  
  
  METHODS
  High resource use outliers were selected according to the following rule: 75th percentile + 1.5* inter-quartile range. Low resource use outliers were selected according to: 25th percentile - 1.5* inter-quartile range. The statistical approach was based on a multivariate analysis using logistic regression. A decision tree approach using predictors from this analysis (intensive care unit (ICU) stay, high severity of illness and social factors associated with longer length of stay) was also tested as a more intuitive tool for use by hospitals in focussing review efforts on "not explained" cost outliers.
  
  
  RESULTS
  High resource use outliers accounted for 6.31% of the hospital stays versus 1.07% for low resource use outliers. The probability of a patient being a high resource use outlier was higher with an increase in the length of stay (odds ratios (OR) = 1.08), when the patient was treated in an intensive care unit (OR = 3.02), with a major or extreme severity of illness (OR=1.46), and with the presence of social factors (OR = 1.44). The probability of being a low outlier is lower for older patients (OR = 0.98). The probability of being a low outlier is also lower without readmission within the year (OR = 0.55). The more intuitive decision tree method identified 92.26% of the cases identified through residuals of the regression model. One quarter of the high cost outliers were flagged for additional review ("not justified" on the basis of the model), with nearly three-quarters "justified" by clinical and social factors.
  
  
  CONCLUSIONS
  The analysis of cost outliers can meet different aims (financing of justifiable outliers, improvement of the care process for the outliers not justifiable on medical or social grounds). The two methods are complementary, by proposing a statistical and a didactic approach to achieve the goal of high quality care using fewer resources.
authors.affiliation: Health Economics Department, School of Public Health, Université Libre de Bruxelles, 806 Route de Lennik, B-1070 Bruxelles, Belgium. magali.pirson@ulb.ac.be
authors.forename: Magali
authors.initials: M
authors.lastname: Pirson
authors.forename: Michèle
authors.initials: M
authors.lastname: Dramaix
authors.forename: Pol
authors.initials: P
authors.lastname: Leclercq
authors.forename: Terri
authors.initials: T
authors.lastname: Jackson
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001530:Belgium; D002648:Child; D002675:Child, Preschool; D005260:Female; D006296:Health Services / Q000706:statistics & numerical data*; D006769:Hospitals, General; D006801:Humans; D007223:Infant; D008297:Male; D008875:Middle Aged; D016311:Outliers, DRG / Q000191:economics*