@inproceedings{9f7c75ec0ab5474ea8d331d996a8c0a1,
title = "A Platform based on Multiple Regression to Estimate the Effect of in-Hospital Events on Total Charges",
abstract = "Recently hospitals struggle to control the cost of care while maintaining optimal outcomes. To respond to this challenge, we developed an interactive web platform which utilizes a multiple linear regression model. The user can create and furthermore alter a clinical scenario, during a patient hospitalization to see the dynamic prediction of total charges, via interactive sessions. The R2 value of our model is 0.655 and the standard error of the estimate is 38,732. Predictors with high coefficient scores include the cardioverter implantation, mechanical ventilation, implant of pulsation balloon and hospital-acquired conditions such as staphylococcus aureus septicemia. Our findings indicate that (a) integration of predictive models into clinical decision support systems is feasible and use of regression methods provide direct feedback on the effect of any clinical practice to the in-hospital charges (b) medical claims data can provide a useful estimation of the in-hospital charges (c) hospital acquired conditions have significant impact on the in-hospital charges.",
keywords = "decision making, multiple linear regression, prediction, total charges",
author = "Dimitrios Zikos and Dhanashri Ostwal",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016 ; Conference date: 04-10-2016 Through 07-10-2016",
year = "2016",
month = dec,
day = "6",
doi = "10.1109/ICHI.2016.72",
language = "English",
series = "Proceedings - 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "403--408",
editor = "Wai-Tat Fu and Kai Zheng and Larry Hodges and Gregor Stiglic and Ann Blandford",
booktitle = "Proceedings - 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016",
}