توضیحات
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.
INTRODUCTION
Data mining can be defined as the process of finding previously unknown patterns and trends in databases and using that information to build predictive models.1 Alternatively, it can be defined as the process of data selection and exploration and building models using vast data stores to uncover previously unknown patterns.2 Data mining is not new—it has been used intensively and extensively by financial institutions, for credit scoring and fraud detection; marketers, for direct marketing and cross-selling or up-selling; retailers, for market segmentation and store layout; and manufacturers, for quality control and maintenance scheduling. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Several factors have motivated the use of data mining applications in healthcare. The existence of medical insurance fraud and abuse, forexample, has led many healthcare insurers to attempt to reduce their losses by using data mining tools to help them find and track offenders.3 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.
Year: 2005
Publishe: SPSS
By: Hian Chye Koh and Gerald Tan
File Information: English Language/ 9Page / size:530KB
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سال :2005
ناشر : SPSS
کاری از : Hian Chye Koh and Gerald Tan
اطلاعات فایل : زبان انگلیسی / 9 صفحه / حجم : 530KB
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