title: Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.
abstract: BACKGROUND
  Case weights of Diagnosis Related Groups (DRGs) are determined by the average cost of cases from a previous billing period. However, a significant amount of cases are largely over- or underfunded. We therefore decided to analyze earning outliers of our hospital as to search for predictors enabling a better grouping under SwissDRG.
  
  
  METHODS
  28,893 inpatient cases without additional private insurance discharged from our hospital in 2012 were included in our analysis. Outliers were defined by the interquartile range method. Predictors for deficit and profit outliers were determined with logistic regressions. Predictors were shortlisted with the LASSO regularized logistic regression method and compared to results of Random forest analysis. 10 of these parameters were selected for quantile regression analysis as to quantify their impact on earnings.
  
  
  RESULTS
  Psychiatric diagnosis and admission as an emergency case were significant predictors for higher deficit with negative regression coefficients for all analyzed quantiles (p<0.001). Admission from an external health care provider was a significant predictor for a higher deficit in all but the 90% quantile (p<0.001 for Q10, Q20, Q50, Q80 and p = 0.0017 for Q90). Burns predicted higher earnings for cases which were favorably remunerated (p<0.001 for the 90% quantile). Osteoporosis predicted a higher deficit in the most underfunded cases, but did not predict differences in earnings for balanced or profitable cases (Q10 and Q20: p<0.00, Q50: p = 0.10, Q80: p = 0.88 and Q90: p = 0.52). ICU stay, mechanical and patient clinical complexity level score (PCCL) predicted higher losses at the 10% quantile but also higher profits at the 90% quantile (p<0.001).
  
  
  CONCLUSIONS
  We suggest considering psychiatric diagnosis, admission as an emergency case and admission from an external health care provider as DRG split criteria as they predict large, consistent and significant losses.
authors.affiliation: Medical Directorate, University Hospital of Zurich, Zürich, Switzerland.
authors.forename: Tarun
authors.initials: T
authors.lastname: Mehra
authors.affiliation: Epidemiology, Biostatistics and Prevention Institute, Department of Biostatistics, University of Zurich, Zurich, Switzerland.
authors.forename: Christian Thomas Benedikt
authors.initials: CT
authors.lastname: Müller
authors.affiliation: Medical Directorate, University Hospital of Zurich, Zürich, Switzerland.
authors.forename: Jörk
authors.initials: J
authors.lastname: Volbracht
authors.affiliation: Epidemiology, Biostatistics and Prevention Institute, Department of Biostatistics, University of Zurich, Zurich, Switzerland.
authors.forename: Burkhardt
authors.initials: B
authors.lastname: Seifert
authors.affiliation: Medical Directorate, University Hospital of Zurich, Zürich, Switzerland.
authors.forename: Rudolf
authors.initials: R
authors.lastname: Moos
mesh_terms: D004469:Economics, Hospital / Q000706:statistics & numerical data; D004636:Emergency Service, Hospital / Q000191:economics / Q000706:statistics & numerical data; D005260:Female; D017721:Hospital Costs / Q000706:statistics & numerical data; D006801:Humans; D007902:Length of Stay / Q000191:economics / Q000706:statistics & numerical data; D008297:Male; D001523:Mental Disorders / Q000175:diagnosis / Q000191:economics; D016311:Outliers, DRG / Q000191:economics / Q000706:statistics & numerical data*; D011445:Prospective Payment System / Q000191:economics / Q000458:organization & administration / Q000706:statistics & numerical data; D013557:Switzerland / Q000453:epidemiology; D062606:Tertiary Care Centers / Q000191:economics / Q000706:statistics & numerical data*