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.
Keywords
Healthcare management ! Customer relationship management ! Healthcare applications ! Data mining methodology and techniques ! Data mining applications ! Predictive modeling
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, for example, 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.
Healthcare Data Mining Applications
There is vast potential for data mining applications in healthcare. Generally, these can be grouped as the evaluation of treatment effectiveness; management of healthcare; customer relationship management; and detection of fraud and abuse. More specialized medical data mining, such as predictive medicine and analysis of DNA micro-arrays, lies outside the scope of this paper. Treatment effectiveness. Data mining applications can be developed to evaluate the effectiveness of medical treatments. By comparing and contrasting causes, symptoms, and courses of treatments, data mining can deliver an analysis of which courses of action prove effective.2 For example, the outcomes of patient groups treated with different drug regimens for the same disease or condition can be compared to determine which treatments work best and are most cost-effective.
Healthcare management.
To aid healthcare management, data mining applications can be developed to better identify and track chronic disease states and high-risk patients, design appropriate interventions, and reduce the number of hospital admissions and claims. For example, to develop better diagnosis and treatment protocols, the Arkansas Data Network looks at readmission and resource utilization and compares its data with current scientific literature to determine the best treatment options, thus using evidence to support medical care.1 Also, the Group Health Cooperative stratifies its patient populations by demographic characteristics and medical conditions to determine which groups use the most resources, enabling it to develop programs to help educate these populations and prevent or manage their conditions.1 Group Health Cooperative has been involved in several data mining efforts to give better healthcare at lower costs. In the Seton Medical Center, data mining is used to decrease patient length-of-stay, avoid clinical complications, develop best practices, improve patient outcomes, and provide information to physicians—all to maintain and improve the quality of healthcare.
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