Perspective Approach Towards Business Intelligence Framework in Healthcare
01 Jan 2018-pp 407-416
TL;DR: This paper attempts to illustrate the BI approaches incorporated with data mining techniques appropriate in the healthcare domain to overcome the issues and challenges more efficiently.
Abstract: Healthcare is highly complex industry driven by knowledge with rising cost and increasing demands for healthcare quality services. Healthcare providers are forced to focus on care quality while minimizing the cost through better healthcare resource management. However the abundant data from different sources such as clinical processes, business processes, and operational processes, causing remarkable issues and challenges are not resolved, through traditional technologies. Thus the Healthcare providers in effort to improve care quality and reduce cost are turning towards advanced and flexible IT-enabled business strategies. This paper attempts to illustrate the BI approaches incorporated with data mining techniques appropriate in the healthcare domain to overcome the issues and challenges more efficiently. Here emphasis is given on the main BI healthcare processes, benefits of using BI strategies in terms of efficiency, care quality and patient satisfaction.
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TL;DR: This article 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.
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.
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