Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center

Tarun Mehra, Christian Thomas Benedikt Müller, Jörk Volbracht, Burkhardt Seifert and Rudolf Moos

PLOS ONE, 2015, vol. 10, issue 10, 1-18

Abstract: Principles: 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

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0140874

DOI: 10.1371/journal.pone.0140874

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