title: Data mining applications in healthcare.
abstract: Data mining has been used intensively and extensively by many organizations. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry. For example, data mining can help healthcare insurers detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. This article explores data mining applications in healthcare. In particular, it discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and the detection of fraud and abuse. It also gives an illustrative example of a healthcare data mining application involving the identification of risk factors associated with the onset of diabetes. Finally, the article highlights the limitations of data mining and discusses some future directions.
authors.affiliation: Nanyang Business School, Nanyang Technological University, Singapore.
authors.forename: Hian Chye
authors.initials: HC
authors.lastname: Koh
authors.forename: Gerald
authors.initials: G
authors.lastname: Tan
mesh_terms: D003258:Consumer Behavior; D017598:Efficiency, Organizational; D005607:Fraud / Q000517:prevention & control; D016247:Information Storage and Retrieval; D008490:Medical Informatics; D016896:Treatment Outcome; D014481:United States