Risks and Rewards Discussion Paper
Risks and Rewards Discussion Paper
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. Risks and Rewards Discussion Paper
Risks and Rewards
Benefits of big data to clinical system
Within the clinical system, big data has typically been processed by machine learning algorithms. In fact, big data has been resultant of health care information digitization and increasing focus on value based care (McGonigle & Mastrian, 2017). This state of affairs has encouraged clinical systems to use data analytics to make strategic decisions with four benefits. Firstly, it helps in predicting outcomes by allowing for precision medicine. Secondly, it improves patient safety and pharmacovigilance by allowing for more informed clinical decisions. Thirdly, it widens the possibility for preventing diseases by identifying the disease risk factors. Finally, it increases early diagnosis and the quality and effectiveness of treatments through reducing possibilities of adverse reactions and discovery of early signals (Pastorino et al., 2019).
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Challenges of big data to clinical system
Big data use in clinical systems poses legal and ethical challenges because of the personal nature of the information involved. These challenges include effectives on public demand for fairness, trust and transparence, as well as risk to compromise personal autonomy and privacy while using big data. While the value of big data is best realized through open use of the data for the patients’ wellbeing within the clinical system, the concerns about health data security, privacy and confidentiality cannot be ignored (Pastorino et al., 2019).
Strategies to address the challenges
There are three strategies of addressing the described challenges. The first strategy is to train and educate health care informaticists on issues of data handling to ensure that they develop the necessary competencies and skills (Pastorino et al., 2019). The second strategy is to apply stringent data handling policies to ensure that only the right persons can access the information they need for delivering improved clinical care and they use that information in a preapproved manner. The third strategy is to apply strict oversight that screens all persons and systems with access to the data so that only authorized access is allowed (McGonigle & Mastrian, 2017).
References
McGonigle, D., & Mastrian, K. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Jones & Bartlett Learning.
Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., & Boccia, S. (2019). Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. European Journal of Public Health, 29(Suppl. 3), 23-27. https://doi.org/10.1093/eurpub/ckz168
Required Readings
McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.
- Chapter 25, “The Art of Caring in Technology-Laden Environments” (pp. 525–535)
- Chapter 26, “Nursing Informatics and the Foundation of Knowledge” (pp. 537–551)
American Nurses Association. (2018). Inclusion of recognized terminologies supporting nursing practice within electronic health records and other health information technology solutions. Retrieved from https://www.nursingworld.org/practice-policy/nursing-excellence/official-position-statements/id/Inclusion-of-Recognized-Terminologies-Supporting-Nursing-Practice-within-Electronic-Health-Records/
Macieria, T. G. R., Smith, M. B., Davis, N., Yao, Y., Wilkie, D. J., Lopez, K. D., & Keenan, G. (2017). Evidence of progress in making nursing practice visible using standardized nursing data: A systematic review. AMIA Annual Symposium Proceedings, 2017, 1205–1214. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977718/
Office of the National Coordinator for Health Information Technology. (2017). Standard nursing terminologies: A landscape analysis. Retrieved from https://www.healthit.gov/sites/default/files/snt_final_05302017.pdf
Rutherford, M. A. (2008). Standardized nursing language: What does it mean for nursing practice? Online Journal of Issues in Nursing, 13(1), 1–12. doi:10.3912/OJIN.Vol13No01PPT05.
Note: You will access this article from the Walden Library databases.
Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs
Topaz, M. (2013). The hitchhiker’s guide to nursing theory: Using the Data-Knowledge-Information-Wisdom framework to guide informatics research. Online Journal of Nursing Informatics, 17(3).
Note: You will access this article from the Walden Library databases.
Wang, Y. Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126(1), 3–13. doi:10.1016/j.techfore.2015.12.019.
Note: You will access this article from the Walden Library databases.
Required Media
Laureate Education (Executive Producer). (2012). Data, information, knowledge and wisdom continuum [Multimedia file]. Baltimore, MD: Author. Retrieved from http://mym.cdn.laureate-media.com/2dett4d/Walden/NURS/6051/03/mm/continuum/index.html
Laureate Education (Producer). (2018). Health Informatics and Population Health: Analyzing Data for Clinical Success [Video file]. Baltimore, MD: Author. Risks and Rewards Discussion Paper
