30 Day hospital readmission analysis

Ratna Madhuri Maddipatla, Mirsad Hadzikadic, Dipti Patel Misra, Lixia Yao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Readmissions to a hospital after procedures are costly and considered to be an indication of poor quality. As Per the Affordable Care Act of 2010, hospitals may be reimbursed at a reduced rate for patients readmitted to a hospital within 30 days of discharge. In this project, we used statistical and machine-learning methods to analyze the Nationwide Inpatient Sample dataset provided by HCUP (Healthcare Cost and Utilization Project) to identify various clinical, demographic and socio-economic factors that play crucial roles in predicting the revenue loss due to readmissions. Three medical conditions, namely chronic obstructive pulmonary disorder (COPD), total hip arthroplasty (THA), and total knee arthroplasty (TKA) have been primarily used for this purpose. Our analysis builds on both non-parametric and parametric statistical models and machine learning techniques such as Decision Tree, Gradient Boosting, Logistic Regression and Neural Networks. We evaluated and compared these models based on Area under ROC (AUC) and misclassification rate. By including visual analytics, this analysis not only enables the hospitals to compute the loss of revenue but also monitors their quality of service in a real-time fashion.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
EditorsFeng Luo, Kemafor Ogan, Mohammed J. Zaki, Laura Haas, Beng Chin Ooi, Vipin Kumar, Sudarsan Rachuri, Saumyadipta Pyne, Howard Ho, Xiaohua Hu, Shipeng Yu, Morris Hui-I Hsiao, Jian Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2922-2924
Number of pages3
ISBN (Electronic)9781479999255
DOIs
StatePublished - Dec 22 2015
Externally publishedYes
Event3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States
Duration: Oct 29 2015Nov 1 2015

Other

Other3rd IEEE International Conference on Big Data, IEEE Big Data 2015
CountryUnited States
CitySanta Clara
Period10/29/1511/1/15

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Keywords

  • Area under ROC (AUC)
  • Data Visualization
  • Hospital Readmissions
  • Misclassification rate
  • Multi Model Evaluation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Software

Cite this

Maddipatla, R. M., Hadzikadic, M., Misra, D. P., & Yao, L. (2015). 30 Day hospital readmission analysis. In F. Luo, K. Ogan, M. J. Zaki, L. Haas, B. C. Ooi, V. Kumar, S. Rachuri, S. Pyne, H. Ho, X. Hu, S. Yu, M. H-I. Hsiao, & J. Li (Eds.), Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015 (pp. 2922-2924). [7364123] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2015.7364123