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

<h4>Principles</h4>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 s...

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Main Authors: Tarun Mehra, Christian Thomas Benedikt Müller, Jörk Volbracht, Burkhardt Seifert, Rudolf Moos
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0140874
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spelling doaj-07295bcd34f941bf8f06f543a85596342021-03-04T07:20:23ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-011010e014087410.1371/journal.pone.0140874Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.Tarun MehraChristian Thomas Benedikt MüllerJörk VolbrachtBurkhardt SeifertRudolf Moos<h4>Principles</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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).<h4>Conclusion</h4>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.https://doi.org/10.1371/journal.pone.0140874
collection DOAJ
language English
format Article
sources DOAJ
author Tarun Mehra
Christian Thomas Benedikt Müller
Jörk Volbracht
Burkhardt Seifert
Rudolf Moos
spellingShingle Tarun Mehra
Christian Thomas Benedikt Müller
Jörk Volbracht
Burkhardt Seifert
Rudolf Moos
Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.
PLoS ONE
author_facet Tarun Mehra
Christian Thomas Benedikt Müller
Jörk Volbracht
Burkhardt Seifert
Rudolf Moos
author_sort Tarun Mehra
title Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.
title_short Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.
title_full Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.
title_fullStr Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.
title_full_unstemmed Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.
title_sort predictors of high profit and high deficit outliers under swissdrg of a tertiary care center.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2015-01-01
description <h4>Principles</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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).<h4>Conclusion</h4>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.
url https://doi.org/10.1371/journal.pone.0140874
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