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.
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 5
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.
In this environment of access to technology at virtually everyone’s fingertips, how do health care professionals make use of big data? In the United States, more than 9 out of 10 people own a cell phone, and globally over 9 out of 10 of the world’s population has access to mobile wireless service (Ng, & Frith, 2018). With so many people on the internet and cell phones, big data is aptly named. The biggest challenge of using big data is how to apply it (Thew, 2016). Once the big data is shorted, big data can make a big difference for many different industries and can potentially be lifesaving in health care. When the correct data is given, disease predictions can occur for healthcare centers (Vijayalakshmi, 2021). This, of course, can be shared with the public due to HIPAA. However, with infectious disease, some of the essential information is reportable and therefore lifesaving.
Ng, Y. C., Alexander, S. & Frith, K. H. (2018). Integration of Mobile Health Applications in Health
Information Technology Initiatives. CIN: Computers, Informatics, Nursing, 36 (5), 209-213. doi:
Thew, J. (2016). Big data means big potential, challenges for nurse execs. Retrieved from
Vijayalakshmi, S., Saini, A., Srinivasan, A., & Singh, N. K. (2021). Disease prediction over big data from
healthcare institutions. 2021 International Conference on Advance Computing and Innovative
Technologies in Engineering (ICACITE), Advance Computing and Innovative Technologies in
Engineering (ICACITE), 2021 International Conference On, 914–919.
By Day 6 of Week 5
Respond to at least two of your colleagues* on two different days, by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.
You bring up some interesting facts about society’s connectivity with their devices. It really shouldn’t come as a surprise, until you stop and actually digest all of the statistics. According to McGonigle and Mastrian (2018), the American healthcare system spends 1.4 trillion dollars on our conventional system. In developing a solution for a new mode, telehealth is an option. The major benefit includes healthcare services without an in-person contact. There are three types of tele health that nurses need to become familiar with. The first is store-and-forward, which deals mainly with video, audio, and images. The second is real time, which is interactive communication. And the last is mHealth, which uses mobile or wireless apps for a patient’s health needs .
One of your points was about HIPPA in reference to big data. This becoming an ever touchy topic as technology continues to evolve. It should come as little surprise that there are rules for the privacy with regards to big data. “Researchers can use PHI if they receive authorizations from the subjects of PHI. Without such authorization, also called informed consent, researchers must apply to the institutional review board for a waiver of authorization. The minimum necessary PHI can be released to researchers without prior authorization from the individual upon the approval of a waiver of authorization by an IRB or a privacy board” (Kayaalp, 2018). However, there are exceptions as the article goes on to point out that researchers can access big data if the person of the PHI is deceased or if research is being done at an institute and the PHI does not leave the premises (Kayaalp, 2018). I take issue with second point. What institution can provide quality research if the data they are accessing is tangible instead of electronic form? So we can deduct that in 2021, most if not all PHI accessed for research is done so electronically. By that standard, what safeguards are in effect for the electronic PHI or what PHI are they using, which is not to leave the institution’s research facility?
Kayaalp M. (2018). Patient Privacy in the Era of Big Data. Balkan medical journal, 35(1), 8–17. https://doi.org/10.4274/balkanmedj.2017.0966
McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning, 366-368.
Great read! it is interesting to know statistics and what is affecting our older population as far as technology. “Roughly 27 percent of Americans over 65 are not online and understanding why is key to changing that. If companies designed devices and software with value for seniors, not as many older people would find themselves on the other side of the digital divide. During a pandemic, that could save lives” (Renstrom, J., 2020).
I am also interested to know whether the use of big data will affect the speed and availability of the internet. How many times do we lose service when attempting to access our systems? Is the use of big data from larger corporations going to slow down the worldwide web?
Our world is fast-moving, and we need to solidify resources that will improve our health and assist us with identifying risk factors that affect our health. Science and technology are the evolving aspects that we must keep up with to maintain our health and well-being. Technology is key and providing education on the necessity of utilizing big data is imperative to everyone’s future.
Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., & Boccia, S. (2019, October 1). Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. European journal of public health. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6859509/.
Renstrom, J. (2020, July 13). Why Older People Really Eschew Technology. (It’s Not Because They Don’t Understand It.). Slate Magazine. https://slate.com/technology/2020/07/seniors-technology-illiteracy-misconception-pandemic.html.
Based on the discussion submitted by Kelly, she highlights how majority of people across the globe has access to mobile and internet, which can easily be used by hospitals as a beneficiary structure. This includes sorting of big data and passing it down to the users through their mobile and internet services to make them aware of information related to any disease. This allows hospitals to make patient aware of the present disease based scenario along with objective that patient’s should follow in order to keep them safe. This information highlighted by Kelly clearly helps identify the correct use of big data reflecting the reward in form of safety. However Kelly failed to recognize the challenges the introduction of this process can bring on the organization or the people. It is because of this reason even though I agree with her first finding of the discussion but believe the submission is incomplete.
Vijayalakshmi, S., Saini, A., Srinivasan, A., & Singh, N. K. (2021). Disease prediction over big data from healthcare institutions. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Advance Computing and Innovative Technologies in Engineering (ICACITE), 2021 International Conference On, 914–919. https://doi-org.ezp.waldenulibrary.org/10.1109/ICACITE51222.2021.9404567
Ng, Y. C., Alexander, S. & Frith, K. H. (2018). Integration of Mobile Health Applications in Health Information Technology Initiatives. CIN: Computers, Informatics, Nursing, 36 (5), 209-213. doi:10.1097/CIN.0000000000000445.
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