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Big Data Risks and Rewards in Healthcare

Big Data Risks and Rewards in Healthcare

The act of analyzing large amounts of data collected from a healthcare data source is known as big data in healthcare. The data could be clinical data, pharmaceutical and R&D data, as well as patient behavior and sentimental data. The use of big data in healthcare aids in the industry’s transition to a value-based model that provides superior patient experiences, outcomes, and treatments (Manogaran & Sundarsekar, 2017).

Data management, data analysis, and electronic medical record keeping are three major advantages of big data in healthcare. Big data assist in locating and identifying the appropriate population or target group. However, it is made up of a diverse population as well as specific groups that can be identified for risk assessment and screenings. The availability of big data enables the creation or modification of a program or intervention to address a health issue. It enables clinical trials to begin right away (Groves & Kuiken, 2016). Big data provides a complete picture of a population’s medical issues. The distribution pattern or disease information allows for the rapid development of intervention programs as well as the early identification of affected groups.

The most difficult challenge of big data is security. Because big data contains personal health history and information, it is critical that the databases remain secret and protected from hacking, phishing, and cyber theft, as stolen information can be sold for a large sum of money. Aside from health information and personal data stolen and hacked from health facilities, other big data from telecommunications, banks, and companies is also vulnerable without the clients’ knowledge. However, before implementing big data, it is critical to ensure that the administration, security, and privacy of the big data are adequately addressed. Anti-virus software, multilayer authentication, data encryption, and the use of firewalls, among other methods, can be used to protect health information. However, the accessibility of healthcare data must be reviewed and monitored on a regular basis (Fang & Iyengar, 2016).

In my experience with big data security in healthcare, there is a method of authentication that is designed to confirm that the subject is true and authentic. The bull eye algorithm has been used to monitor sensitive data from all sides. The algorithm was used to ensure data security as well as to manage relationships between original and replicated data. Only authorized individuals are permitted to read or write critical data (Groves & Kuiken, 2016). However, in the healthcare system, both healthcare data provided by providers and consumer identities must be verified at the point of entry.


Groves, P., Kayyali, B., Knott, D., & Kuiken, S. V. (2016). The big data revolution in healthcare: Accelerating value and innovation.

Fang, R., Pouyanfar, S., Yang, Y., Chen, S. C., & Iyengar, S. S. (2016). Computational health informatics in the big data age: a survey. ACM Computing Surveys (CSUR), 49(1), 12.

Manogaran, G., Thota, C., Lopez, D., Vijayakumar, V., Abbas, K. M., & Sundarsekar, R. (2017). Big data knowledge system in healthcare. In the Internet of things and big data technologies for next generation healthcare (pp. 133-157). Springer, Cham.


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When you wake in the morning, you may reach for your cell phone to reply to a few text 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.

Big Data Risks and Rewards in Healthcare

Big Data Risks and Rewards in Healthcare

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, and 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.

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 the big data you described. Be specific and provide examples.

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