TY - GEN
T1 - 30 Day hospital readmission analysis
AU - Maddipatla, Ratna Madhuri
AU - Hadzikadic, Mirsad
AU - Misra, Dipti Patel
AU - Yao, Lixia
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/22
Y1 - 2015/12/22
N2 - 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.
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.
KW - Area under ROC (AUC)
KW - Data Visualization
KW - Hospital Readmissions
KW - Misclassification rate
KW - Multi Model Evaluation
UR - http://www.scopus.com/inward/record.url?scp=84963743388&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963743388&partnerID=8YFLogxK
U2 - 10.1109/BigData.2015.7364123
DO - 10.1109/BigData.2015.7364123
M3 - Conference contribution
AN - SCOPUS:84963743388
T3 - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
SP - 2922
EP - 2924
BT - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
A2 - Luo, Feng
A2 - Ogan, Kemafor
A2 - Zaki, Mohammed J.
A2 - Haas, Laura
A2 - Ooi, Beng Chin
A2 - Kumar, Vipin
A2 - Rachuri, Sudarsan
A2 - Pyne, Saumyadipta
A2 - Ho, Howard
A2 - Hu, Xiaohua
A2 - Yu, Shipeng
A2 - Hsiao, Morris Hui-I
A2 - Li, Jian
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Big Data, IEEE Big Data 2015
Y2 - 29 October 2015 through 1 November 2015
ER -