Classmate Responses

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.

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.

Response 1

Big data refers to data collections frequently evaluated using modern computing techniques to reveal information about human activities, trends, and patterns. Big data helps the healthcare industry adapt faster data processing speeds, enabling individuals to obtain pertinent insights that aid in the treatment they provide for patients. One of the primary benefits of using big data in a clinical system is that care providers can constantly and efficiently monitor patients' vital signs. Healthcare providers frequently attempt to improve the health of patients suffering from chronic diseases by monitoring their vitals, such as temperature and blood pressure. The development of big data technology, such as electronic health records, has facilitated the collection and examination of demographic and medical data by healthcare practitioners. Because of this, they can make informed clinical judgments that ensure quality care is provided. Lastly, implementing this technology has improved mental health departments' administrative and financial efficiency, contributing to a decline in the incidence of medical errors (Ngiam & Khor, 2019).

The inability to record all the data presents one of the most significant obstacles that big data must overcome before it can be successfully implemented in a therapeutic setting. Big data cannot participate in the accurate diagnosis of patient diseases. Also, because big data is utilized in making and monitoring prescriptions, technological systems may not have effective ways of recording patient symptoms and the reaction of patients to medications. Most healthcare professionals who use big data are only concerned with a small portion of the reported information, and they are unaware of the possibility that there is additional data that big data does not account (Hariri et al., 2019). As a result, the problem is not always recognized when it occurs.

Prioritizing data types is one of the tactics that may be used to help alleviate the difficulty of obtaining information in a clinical system employing big data. Big data may involve a scenario in which computers take on human functions, yet, it is clear that people are the ones who program these computers to collect and record data in a particular way. Even though it is a potent technological instrument for managing data, there are still people's minds behind its creation of it. When these programmers make sure that they prioritize the sorts of data that are open for documentation, they can solve the problem of massive data and, as a result, lower the likelihood that the system will leave out some of the relevant patient information (Akila1 et al., 2022). Additionally, clinical documentation and improvement initiatives that assist physicians in ensuring complete data capture by the systems are essential. This kind of education could be geared toward helping the people who provide healthcare have the skills to perform constant improvement and imparting the skills necessary to modify the system in formats that help relay a significant amount of patient information to the people who provide care. The performance of the system can also be improved through the use of big data by consistently monitoring any changes that have been implemented (Kashyap, 2019).

References

Akila1, A., Parameswari, R., & Jayakumari, C. (2022). Big Data in Healthcare: Management, analysis, and future prospects. Handbook of Intelligent Healthcare Analytics, 309–326. https://doi.org/10.1002/9781119792550.ch14

Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: Survey, opportunities, and challenges. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0206-3

Kashyap, R. (2019). Big Data Analytics Challenges and Solutions. Big Data Analytics for Intelligent Healthcare Management, 19–41. https://doi.org/10.1016/b978-0-12-818146-1.00002-7

Ngiam, K. Y., & Khor, I. W. (2019). Big Data and machine learning algorithms for healthcare delivery. The Lancet Oncology, 20(5). https://doi.org/10.1016/s1470-2045(19)30149-4

Response 2

Initial Post

Using big data in nursing can help improve the patient's care and advance the practice of the nurse. The use of big data can positively influence nursing career practice through research when combined with other claims. Big data promises the growth of artificial companies and patients' healthcare (Cohen & Mello, 2019). Big data helps to take notice of trends and patterns in healthcare. Nurses need to maximize the advantage of big data. This helps research and planning appropriately for the patients through the research results and possible solutions. This will help in promoting and advancing the care and wellness of human beings the nurses.

The risk of big data in nursing includes the possibility of the data being hacked by cyber attackers. This means that the privacy of the patient's data will have been breached (Cassel & Bindman, 2019). This is one disadvantage of big data, which places the nurse at risk of facing law charges. One way the big data challenge can be solved is by having unique identification codes for specific healthcare team members to protect the patient's data. Shutting down devices in the hospital when not in use can also help to protect the data from cyber attackers. This will help in promoting the privacy of patients' data.

Cassel, C., & Bindman, A. (2019). Risk, Benefit, and Fairness in a Big Data World. JAMA, 322(2), 105–106. Retrieved December 25, 2022, from https://doi.org/10.1001/jama.2019.9523

Cohen, I. G., & Mello, M. M. (2019). Big Data, Big Tech, and Protecting Patient Privacy. JAMA, 322(12), 1141–1142. Retrieved December 25, 2022, from https://doi.org/10.1001/jama.2019.11365