Title :
Predicting Future High-Cost Patients: A Real-World Risk Modeling Application
Author :
Moturu, Sai T. ; Johnson, William G. ; Liu, Huan
Author_Institution :
Arizona State Univ., Tempe
Abstract :
Health care data from patients in the Arizona Health Care Cost Containment System, Arizona´s Medicaid program, provides a unique opportunity to exploit state-of-the-art data processing and analysis algorithms to mine the data and provide actionable results that can aid cost containment. This work addresses specific challenges in this real-life health care application to build predictive risk models for forecasting future high-cost users. Such predictive risk modeling has received attention in recent years with statistical techniques being the backbone of proposed methods. We survey the literature and propose a novel data mining approach customized for this potent application. Our empirical study indicates that this approach is useful and can benefit further research on cost containment in the health care industry.
Keywords :
data mining; health care; medical administrative data processing; medical computing; patient care; probability; risk analysis; statistical analysis; Arizona Health Care Cost Containment System; Arizona Medicaid program; data analysis algorithm; data mining; data processing algorithm; health care; high cost patients; predictive risk modeling; real world risk modeling; statistical techniques; Application software; Bioinformatics; Biomedical engineering; Biomedical informatics; Computer science; Costs; Data analysis; Data mining; Medical services; Predictive models;
Conference_Titel :
Bioinformatics and Biomedicine, 2007. BIBM 2007. IEEE International Conference on
Conference_Location :
Fremont, CA
Print_ISBN :
978-0-7695-3031-4
DOI :
10.1109/BIBM.2007.54