Data mining applications in healthcare.

• Main Authors: Koh, Hian Chye., Tan, Gerald.
• Other Authors: Nanyang Business School
• Format: Research Report
• Published: 2008
• Subjects: DRNTU::Business
• Online Access: http://hdl.handle.net/10356/7039
• Institution: Nanyang Technological University

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 mass 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 massive data into useful information for decision making. This paper explores data mining applications in healthcare. In particular, it discusses data mining and its healthcare applications in major categories such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and 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 paper highlights the limitations of data mining and discusses some future directions.