Posted: March 28th, 2021

Discussion: Big Data Risks and Rewards

Discussion: Big Data Risks and Rewards
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.
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.

Assignment Essay Example

Big Data Risks and Rewards
Big data is used in a clinical system to improve operational efficiency. The data gathered about the patients’ admission, the disease, the staffing ratios, and nurse efficiency helps in decision making. Therefore, an organization will use data to evaluate the quality operational efficiency (Kruse, Goswamy, Raval & Marawi, 2016). For example, the data can be used to eliminate delays due to a shortage of staff. It will also examine the history of patients’ records and thus predict the future and prepare for it. In this case, the operational efficiency will be enhanced all the time.
One of the challenges or risks of using big data in healthcare is the safety of the data. The data in health organizations is at risk of being hacked, stolen or tampered with. An organization requires various security systems to prevent cyber attacks (Raghupathi & Raghupathi, 2014). If data is lost from the clinical system, it would expose patients to the risk of exposing their phone numbers, home address and medical conditions to suspicious people. For example, health organizations have started embracing blockchains to enhance the security of big data generated in health organizations. Blockchain allows safe and efficient sharing of data.
Big data can be secured through employee training on safety measures. Employees interact with the data and share it across various platforms. They need to be trained on double authentication, signing-off policies, keeping passwords secure and minimizing unauthorized access (Lee & Yoon, 2017). Additionally, they should be taught on how to audit their data to check if there are attempts to tamper with it. For example, organizations are training staff on using time-stamped files to ensure they identify any suspicious activity that took place in their absence. Therefore, training will equip nurses to keep off hackers and preserve bid data.

Kruse, C. S., Goswamy, R., Raval, Y. J., & Marawi, S. (2016). Challenges and opportunities of big data in health care: a systematic review. JMIR medical informatics, 4(4), e38.
Lee, C. H., & Yoon, H. J. (2017). Medical big data: promise and challenges. Kidney research and clinical practice, 36(1), 3.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), 3.


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