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Big Data Risks And Rewards in Healthcare

Big Data Risks And Rewards in Healthcare 

This report is an analysis of the risks and rewards of big data in the healthcare sector. The focus is on insurance companies offering healthcare services to various consumers.

When you wake in the morning, you may reach for your cell phone to reply to a few texts or email messages that you missed overnight. On your drive to work, you may stop to refuel your car. Upon your arrival, you might swipe a key card at the door to gain entrance to the facility. And before finally reaching your workstation, you may stop by the cafeteria to purchase a coffee.

From the moment you wake, you are in fact a data-generation machine. Each use of your phone, every transaction you make using a debit or credit card, even your entrance to your place of work, creates data. It begs the question: How much data do you generate each day? Many studies have been conducted on this, and the numbers are staggering: Estimates suggest that nearly 1 million bytes of data are generated every second for every person on earth.

As the volume of data increases, information professionals have looked for ways to use big data—large, complex sets of data that require specialized approaches to use effectively. Big data has the potential for significant rewards—and significant risks—to healthcare. In this Discussion, you will consider these risks and rewards.

To Prepare:

Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs.
Reflect on your own experience with complex health information access and management and consider potential challenges and risks you may have experienced or observed.
By Day 3 of Week 4
Post a description of at least one potential benefit of using big data as part of a clinical system and explain why. Then, describe at least one potential challenge or risk of using big data as part of a clinical system and explain why. Propose at least one strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using big data you described. Be specific and provide examples

Big data risks 

Insurance companies offering health services are currently facing severe losses due to fraud arising from the healthcare sector. In the USA alone, more than $ 100 million dollars are lost annually by the insurance, a value that is only 10% the annual health care expenditure in the country (Ogwueleka, 2011). The amount of money lost by the insurance firms in the UK and other continents is expected to be higher due to the increasing cases of fraud within the health care sector that are reported. According to Rawte and Anuradha (2015), fraud in health care occurs when the physicians and health care practitioners bill patients for undelivered services, over-bill the patient, perform unwarranted medical services to attract insurance pay or accept “kickbacks” to benefit from patient referral. These actions are very evident in most of the health care facilities in different regions across the world. Apart from resulting in huge loses being made by the insurance firms, fraud in the healthcare sector also leads to the loss of capital that would have been used in the provision of quality health care services to the patients.
The increasing cases of fraud within the healthcare sector have instigated the need for the implementation of strategies to curb the menace. According to Rawte and Anuradha (2015), intelligent data mining techniques are the main approach that has been widely adopted to detect fraud in health care and consequently take action to prevent future occurrence. The complexity and huge nature of the healthcare transactions require the use of a robust information technology system that will support accurate validation of the transactions, thus the adoption of intelligent data mining techniques. According to Bănărescu (2015) intelligent data mining techniques allows for the discovery of unknown patterns and relations that signify fraudulent activities. The techniques also support the incorporation of the knowledge discovered as a business rule for future screening of any emerging transactions to identify and block fraudulent transactions in real-time.
Of the many intelligent data mining techniques applicable in fraud detection, no evidence has been availed on the most effective approach. In fact, most scholars have argued that relying on a single data mining is not likely to yield quality findings. Rather, a hybrid data mining technique including two or more supervised or unsupervised data mining techniques is needed to enhance the effectiveness of the process (Zhang & Zhang, 2004). A number of scholars have proposed different forms of hybrid data mining techniques that can be adapted to detect fraud in healthcare; however, there is limited information on the effectiveness or practical evidence of the system. The theoretical assertion that the hybrid intelligent data mining techniques are likely to offer better results needs to be tested practically, to justify their adoption in the health care sector. This forms the basis of the proposed study that seeks to test the effectiveness of a hybrid data mining technique in the discovery of unknown patterns in a set of data collected from various health care facilities that are likely to signify fraudulent activities.