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 this Discussion: Big Data Risks and Rewards NURS 6051:
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.
Discussion: Big Data Risks and Rewards NURS 6051
By Day 3 of Week 5Post 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.
By Day 6 of Week 5Respond 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 Discussion: Big Data Risks and Rewards NURS 6051.
*Note: Throughout this program, your fellow students are referred to as colleagues.
Big Data: Potential Benefits and Risks Example
Big Data in nursing practice has no unified definition. However, the baseline meaning of Big Data corresponds to the enormous size of data regarding volume, velocity, variety, and veracity (Wong et al., 2016).
The advancement of technology and clinical research studies has increased the amount of data handled in every facet of nursing practice. The use of big data in the clinical system has potential benefits and risks. This paper will discuss the potential benefits and risks associated with the utilization of big data in nursing clinical systems and propose one strategy to prevent the risks.
Potential Benefit of Using Big Data
In the nursing practice, big data sources include the nursing clinical research findings, patient medical records, and results of clinical examination and laboratory investigations, including imaging. The application of big data concepts in the healthcare industry aims at improving the quality of healthcare outcomes by revolutionizing and modernizing healthcare practice (Agrawal & Prabakaran, 2020).
Including technology in handling big data in nursing and clinical research has potential benefits for future healthcare. Analysis and utilization of big data will positively impact healthcare quality by increasing its effectiveness while reducing costs.
Secondly, insight descriptive analysis of big data yields diagnostic data that result in predictive outcomes. The predictive outcomes yield prescriptive results that lead to smarter and cost-effective health outcomes (Dash et al., 2019).
This can happen in four different ways: early risk factor determination; early determination of markers or signals of adverse situations of disease or intervention; timely decision making based on analyzed past data; and ability to predict future outcomes of diseases (Pastorino et al., 2019). Through these four ways, big data ensures timely diagnosis and effectiveness of interventions, improved patient safety and pharmaco-vigilance, and disease prevention.
Scrutinizing big data differs from a secondary healthcare data analysis in the health setting. The intention of big data analysis and utilization of this data with undiscovered scope or size is to answer certain research questions and uncover certain disease conditions that are yet to be fully epidemiologically described. Therefore, big data has the potential of changing the course and practice of medicine and nursing by making them more preventive, diagnostic, and therapeutic (Ienca et al., 2018).
Potential Challenge or Risk of Using Big Data
Despite the described benefits of big data, it is still unclear whether it is the answer to the limited quality of care delivery and access faced in different global contexts. The concept of unmeasured confounding makes determinations of the statistical associations in causations Discussion: Big Data Risks and Rewards NURS 6051.
The increase in the size of data increases the chances of biases in data sets and making inferences from the analyzed big data. Various other confounders can cause a high variation in correlations from the big data analyses. As a new concept of data mining and utilization in the clinical system, big data analysis will require specialized advanced technology and skills that are yet to be widespread among clinical researchers and clinicians (Wong et al., 2016).
Further, digital maturity in healthcare lags compared to other fields (Suter-Crazzolara, 2018). There are ethical implications and violations that come with the utilization of big data, such as privacy, gender discrimination, and data protection (Alexandru et al., 2018). A study by Ienca et al. (2018) suggests a solution to the ethical challenges that entails scrutinizing biomedical research using big data regarding social benefits, data control, accountability, purpose, ability, and intention to share. While solvable, the methodological and ethical risks that come with the utilization of big data tend to require proper scrutinization.
Conclusion
The big data concept implies the enormous volume, veracity, and velocity of ever-increasing data available in biomedical research and data analysis Discussion: Big Data Risks and Rewards NURS 6051.
The benefit of the utilization of big data would reduce the cost of healthcare while increasing its effectiveness and timeliness of care. However, there still exist challenges of technical limitations and risks of ethical violations in big data use, which, alongside the apparent lag in big data usage in clinical settings, might jeopardize future integration into nursing practice.
References
Agrawal, R., & Prabakaran, S. (2020). Big data in digital healthcare: lessons learned and recommendations for general practice. Heredity, 124(4), 525-534. https://doi.org/10.1038/s41437-020-0303-2
Alexandru, A., Radu, I., & Bizon, M. (2018). Big Data in Healthcare – Opportunities and Challenges. Informatica Economica, 22(2/2018), 43-54. https://doi.org/10.12948/issn14531305/22.2.2018.05
Dash, S., Shakyawar, S., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis, and future prospects. Journal Of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0217-0
Ienca, M., Ferretti, A., Hurst, S., Puhan, M., Lovis, C., & Vayena, E. (2018). Considerations for ethics review of big data health research: A scoping review. PLOS ONE, 13(10), e0204937. https://doi.org/10.1371/journal.pone.0204937
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(Supplement_3), 23-27. https://doi.org/10.1093/eurpub/ckz168
Wong, H., Chiang, V., Choi, K., & Loke, A. (2016). The Need for a Definition of Big Data for Nursing Science: A Case Study of Disaster Preparedness. International Journal Of Environmental Research And Public Health, 13(10), 1015. https://doi.org/10.3390/ijerph13101015
Submission and Grading Information for Discussion: Big Data Risks and Rewards NURS 6051
Grading Criteria
To access your rubric:
Week 5 Discussion Rubric
Post by Day 3 and Respond by Day 6 of Week 5
To participate in this Discussion:
Week 5 Discussion
Required Readings for Discussion: Big Data Risks and Rewards NURS 6051
McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.
Chapter 22, “Data Mining as a Research Tool” (pp. 477-493)
Chapter 24, “Bioinformatics, Biomedical Informatics, and Computational Biology” (pp. 537-551)
Discussion: Big Data Risks and Rewards NURS 6051 Rubric
MAIN POSTING–
Excellent 45 (45%) – 50 (50%)
Answers all parts of the discussion question(s) expectations with reflective critical analysis and synthesis of knowledge gained from the course readings for the module and current credible sources.
Supported by at least three current, credible sources. Written clearly and concisely with no grammatical or spelling errors and fully adheres to current APA manual writing rules and style.Good 40 (40%) – 44 (44%)
Responds to the discussion question(s) and is reflective with critical analysis and synthesis of knowledge gained from the course readings for the module.
At least 75% of post has exceptional depth and breadth. Supported by at least three credible sources. Written clearly and concisely with one or no grammatical or spelling errors and fully adheres to current APA manual writing rules and style.Fair 35 (35%) – 39 (39%)
Responds to some of the discussion question(s).
One or two criteria are not addressed or are superficially addressed. Is somewhat lacking reflection and critical analysis and synthesis. Somewhat represents knowledge gained from the course readings for the module. Post is cited with two credible sources. Written somewhat concisely; may contain more than two spelling or grammatical errors. Contains some APA formatting errors.Poor 0 (0%) – 34 (34%)
Does not respond to the discussion question(s) adequately.
Lacks depth or superficially addresses criteria. Lacks reflection and critical analysis and synthesis. Does not represent knowledge gained from the course readings for the module. Contains only one or no credible sources. Not written clearly or concisely. Contains more than two spelling or grammatical errors. Does not adhere to current APA manual writing rules and style.
NURS 6051 Module 4: Technologies Supporting Applied Practice and Optimal Patient Outcomes (Weeks 6-8)
Laureate Education (Producer). (2018). Informatics Tools and Technologies [Video file]. Baltimore, MD: Author.
Accessible player –Downloads– Download Video w/CC Download Audio Download Transcript
Learning Objectives
Students will:
Evaluate healthcare technology trends for data and information in nursing practice and healthcare delivery
Analyze challenges and risks inherent in healthcare technology
Analyze healthcare technology benefits and risks for data safety, legislation, and patient care
Evaluate healthcare technology impact on patient outcomes, efficiencies, and data management
Analyze research on the application of clinical systems to improve outcomes and efficiencies
Discussion: Big Data Risks and Rewards NURS 6051
Due By
Assignment
Week 6, Days 1–2
Read/Watch/Listen to the Learning Resources.
Compose your initial Discussion post.
Week 6, Day 3
Post your initial Discussion post.
Begin to compose your Assignment.
Week 6, Days 4-5
Review peer Discussion posts.
Compose your peer Discussion responses.
Continue to compose your Assignment.
Week 6, Day 6
Post at least two peer Discussion responses on two different days (and not the same day as the initial post).
Week 6, Day 7
Wrap up Discussion.
Week 7, Days 1-7
Continue to compose your Assignment.
Week 8, Days 1-6
Continue to compose your Assignment.
Week 8, Day 7
Deadline to submit your Assignment.
Learning Resources
Required Readings
McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.
Chapter 14, “The Electronic Health Record and Clinical Informatics” (pp. 267–287)
Chapter 15, “Informatics Tools to Promote Patient Safety and Quality Outcomes” (pp. 293–317)
Chapter 16, “Patient Engagement and Connected Health” (pp. 323–338)
Chapter 17, “Using Informatics to Promote Community/Population Health” (pp. 341–355)
Chapter 18, “Telenursing and Remote Access Telehealth” (pp. 359–388)
