30 Day hospital readmission analysis

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

doi:10.1109/BigData.2015.7364123

30 Day hospital readmission analysis

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

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
Editors	Feng 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
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2922-2924
Number of pages	3
ISBN (Electronic)	9781479999255
DOIs	https://doi.org/10.1109/BigData.2015.7364123
State	Published - Dec 22 2015
Externally published	Yes
Event	3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States Duration: Oct 29 2015 → Nov 1 2015

Other

Other	3rd IEEE International Conference on Big Data, IEEE Big Data 2015
Country/Territory	United States
City	Santa Clara
Period	10/29/15 → 11/1/15

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

Access to Document

10.1109/BigData.2015.7364123

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

30 Day hospital readmission analysis. / Maddipatla, Ratna Madhuri; Hadzikadic, Mirsad; Misra, Dipti Patel et al.

Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. ed. / Feng 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. Institute of Electrical and Electronics Engineers Inc., 2015. p. 2922-2924 7364123.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Maddipatla, RM, Hadzikadic, M, Misra, DP & Yao, L 2015, 30 Day hospital readmission analysis. in F Luo, K Ogan, MJ Zaki, L Haas, BC Ooi, V Kumar, S Rachuri, S Pyne, H Ho, X Hu, S Yu, MH-I Hsiao & J Li (eds), Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015., 7364123, Institute of Electrical and Electronics Engineers Inc., pp. 2922-2924, 3rd IEEE International Conference on Big Data, IEEE Big Data 2015, Santa Clara, United States, 10/29/15. https://doi.org/10.1109/BigData.2015.7364123

Maddipatla RM, Hadzikadic M, Misra DP, Yao L. 30 Day hospital readmission analysis. In Luo F, Ogan K, Zaki MJ, Haas L, Ooi BC, Kumar V, Rachuri S, Pyne S, Ho H, Hu X, Yu S, Hsiao MH-I, Li J, editors, Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 2922-2924. 7364123 doi: 10.1109/BigData.2015.7364123

Maddipatla, Ratna Madhuri ; Hadzikadic, Mirsad ; Misra, Dipti Patel et al. / 30 Day hospital readmission analysis. Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. editor / Feng 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. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 2922-2924

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AB - 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.

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30 Day hospital readmission analysis

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Keywords

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