TY - GEN
T1 - Cluster Analysis on Utilization Patterns of Patients with Chronic Diseases Based on Flattened Electronic Medical Records
AU - Wang, Debby D.
AU - Quan, Tan Xin
AU - Khoo, Astrid
AU - Sridharan, Srinath
AU - Ramachandran, Sravan
AU - Ng, Sheryl Hui Xian
AU - Rahman, Siti Nabilah Abdul
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Designing efficient strategies of reducing health care costs and optimizing facility utilization has long be a pursue of health systems, and stratifying patients into different expenditure/utilization patterns and further profiling these patient groups can provide useful clues for the strategy design. In this study, a data set including patients with chronic diseases (hypertension, diabetes, heart failure, chronic obstructive pulmonary disease (COPD) and/or stroke) was constructed, based on flattening massive electronic medical records and aggregating such records into the patient level. After outlier removal, the total cost and length-of-stay (LOS) of patients were used to cluster the patients into high-cost utilizers and low-cost ones, depending on k-medoids clustering with subsampling validation. To further profile or compare the two patient clusters, median-cost and median-LOS difference between the patients with a specific type or number of disease in the high-cost cluster and those in the low-cost one were investigated. Ultimately, COPD corresponds to the highest cost/LOS difference among the five diseases, implying that it is a potentially important disease that drives high costs/LOS. Moreover, patients with all five diseases correspond to the highest cost/LOS difference compared to those with one to four diseases. Overall, this study can usefully promote the development of healthcare-strategy design and implementation.
AB - Designing efficient strategies of reducing health care costs and optimizing facility utilization has long be a pursue of health systems, and stratifying patients into different expenditure/utilization patterns and further profiling these patient groups can provide useful clues for the strategy design. In this study, a data set including patients with chronic diseases (hypertension, diabetes, heart failure, chronic obstructive pulmonary disease (COPD) and/or stroke) was constructed, based on flattening massive electronic medical records and aggregating such records into the patient level. After outlier removal, the total cost and length-of-stay (LOS) of patients were used to cluster the patients into high-cost utilizers and low-cost ones, depending on k-medoids clustering with subsampling validation. To further profile or compare the two patient clusters, median-cost and median-LOS difference between the patients with a specific type or number of disease in the high-cost cluster and those in the low-cost one were investigated. Ultimately, COPD corresponds to the highest cost/LOS difference among the five diseases, implying that it is a potentially important disease that drives high costs/LOS. Moreover, patients with all five diseases correspond to the highest cost/LOS difference compared to those with one to four diseases. Overall, this study can usefully promote the development of healthcare-strategy design and implementation.
UR - http://www.scopus.com/inward/record.url?scp=85047420917&partnerID=8YFLogxK
U2 - 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.49
DO - 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.49
M3 - Conference contribution
AN - SCOPUS:85047420917
T3 - Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017
SP - 293
EP - 298
BT - Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017
A2 - Wu, Yulei
A2 - Min, Geyong
A2 - Georgalas, Nektarios
A2 - Al-Dubi, Ahmed
A2 - Jin, Xiaolong
A2 - Yang, Laurence T.
T2 - Joint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017
Y2 - 21 June 2017 through 23 June 2017
ER -
