Predictive modeling

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. Original Contributions Journal of Healthcare Information Management — Vol. 19, No. 2 65 reduce their losses by using data mining tools to help them find and track offenders. Fraud detection using data mining applications is prevalent in the commercial world, for example, in the detection of fraudulent credit card transactions. Recently, there have been reports of successful data mining applications in healthcare fraud and abuse detection. Another factor is that the huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. Such analysis has become increasingly essential as financial pressures have heightened the need for healthcare organizations to make decisions based on the analysis of clinical and financial data. Insights gained from data mining can influence cost, revenue, and operating efficiency while maintaining a high level of care. Healthcare organizations that perform data mining are better positioned to meet their long-term needs, Benko and Wilson argue. Data can be a great asset to healthcare organizations, but they have to be first transformed into information. Yet another factor motivating the use of data mining applications in healthcare is the realization that data mining can generate information that is very useful to all parties involved in the healthcare industry. For example, data mining applications can help healthcare insurers detect fraud and abuse, and healthcare providers can gain assistance in making decisions, for example, in customer relationship management. Data mining applications also can benefit healthcare providers, such as hospitals, clinics and physicians, and patients, for example, by identifying effective treatments and best practices. There are also other factors boosting data mining’s popularity. For instance, as a result of the Balanced Budget Act of 1997, the Centers for Medicare and Medicaid Services must implement a prospective payment system based on classifying patients into case-mix groups, using empirical evidence that resource use within each case-mix group is relatively constant. CMS has used data mining to develop a prospective payment system for inpatient rehabilitation. The healthcare industry can benefit greatly from data mining applications. The objective of this article is to explore relevant data mining applications by first examining data mining methodology and techniques; then, classifying potential data mining applications in healthcare; next, giving an illustration of a healthcare data mining application; and finally, highlighting the limitations of data mining and offering some future directions.