%0 Journal Article %A Giorgia Lüthi-Corridori %A Stéphanie Giezendanner %A Philippe Salathé %A Jörg Leuppi %T Machine learning-based prediction of deviations from DRG-based average length of hospital stay (LOHS) in a Swiss hospital %D 2021 %R 10.1183/13993003.congress-2021.OA4282 %J European Respiratory Journal %P OA4282 %V 58 %N suppl 65 %X Background: Since 2012, Switzerland has a prospective hospital payment method based on diagnosis‐related groups (DRGs), reimbursing a flat rate per case. Deviations from the DRG defined length of hospital stay (LOHS) may indicate deficiencies in quality or management and may result in an economic burden for a hospital.Aim: The aim was to identify predictor variables for deviations from DRG average LOHS using machine learning.Methods: The research was based on existing data of a Swiss cantonal Hospital (KSBL). All patients hospitalized for more than one day from 2015 were included in the study. As possible predictors, we included sociodemographic, diseases and treatment-related characteristics. We used penalized linear regression with Ridge and Lasso regularization and ten-fold cross-validation to train each model.Results: A total of 87,706 patients and 114 features were entered into the models. The predictive efficiency of the optimal combinations of features by Lasso and Ridge were almost equal explaining 24% of the total variance in LOHS deviations. Patients who needed geriatric rehabilitation or presented with cerebrovascular disorders stayed longer than expected (β=2.45 and β=0.69 SD above the DRG average). The analysis of the diseases related to the respiratory system (ICD Chapter 10) showed that patients with acute bronchitis, Asthma bronchiale and Influenza had a higher LOHS than expected by DRG.Relevance: Evidence-based hospital management combined with machine learning will enable new strategies to reduce costs and increase the quality empowering personalized health care services.FootnotesCite this article as: European Respiratory Journal 2021; 58: Suppl. 65, OA4282.This abstract was presented at the 2021 ERS International Congress, in session “Prediction of exacerbations in patients with COPD”.This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only). %U