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

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

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

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  • Tarun Mehra & Christian Thomas Benedikt Müller & Jörk Volbracht & Burkhardt Seifert & Rudolf Moos, 2015. "Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-18, October.
    • Handle: RePEc:plo:pone00:0140874
    DOI: 10.1371/journal.pone.0140874
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    References listed on IDEAS

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    1. Pirson, Magali & Dramaix, Michele & Leclercq, Pol & Jackson, Terri, 2006. "Analysis of cost outliers within APR-DRGs in a Belgian general hospital: Two complementary approaches," Health Policy, Elsevier, vol. 76(1), pages 13-25, March.
    2. Cots, Francesc & Mercade, Lluc & Castells, Xavier & Salvador, Xavier, 2004. "Relationship between hospital structural level and length of stay outliers: Implications for hospital payment systems," Health Policy, Elsevier, vol. 68(2), pages 159-168, May.
    3. Tsair-Fwu Lee & Pei-Ju Chao & Hui-Min Ting & Liyun Chang & Yu-Jie Huang & Jia-Ming Wu & Hung-Yu Wang & Mong-Fong Horng & Chun-Ming Chang & Jen-Hong Lan & Ya-Yu Huang & Fu-Min Fang & Stephen Wan Leung, 2014. "Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
    4. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
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    Cited by:

    1. Fabrizia Schmid & Alexandra Malinovska & Karin Weigel & Tito Bosia & Christian H Nickel & Roland Bingisser, 2019. "Construct validity of acute morbidity as a novel outcome for emergency patients," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-10, January.
    2. Subelack, Jonas, 2025. "Swiss hospital financing with DRGs: Are there treatments/- combinations that are associated with profitability?," Working Paper Series in Health Economics, Management and Policy 2025-02, University of St.Gallen, School of Medicine, Chair of Health Economics, Policy and Management.

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