Discussion: Big Data Risks and Rewards NURS 6051When you wake in the morning, you may reach for your

Discussion: Big Data Risks and Rewards NURS 6051

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.

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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 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.

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 Discussion: Big Data Risks and Rewards NURS 6051.

*Note: Throughout this program, your fellow students are referred to as colleagues.

Discussion: Big Data Risks and Rewards NURS 6051 Example 1

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.

Discussion: Big Data Risks and Rewards NURS 6051 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

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

Discussion: Big Data Risks and Rewards NURS 6051 Example 2

Adopting new healthcare technology as information technology also advances comes with big data. According to Dash et al. (2019), big data means the enormous amount of information created by adopting technologies that collect patients’ records, which require new technology to capture, store, analyze, and assist decision-making, optimize processes, and manage hospital performance.

Big data may have different potential benefits and challenges. However, some strategies can be used to overcome the risks. This discussion presents the potential benefits and challenges of using big data as part of a clinical system and the reasons behind these potential benefits or challenges. It will also propose a strategy to mitigate big data’s potential challenges or risks effectively.

The potential benefits of having big data as a part of a clinical system include improved research and better patient care. As mentioned earlier, big data means enormous amounts of information. When researchers have large volumes of information, they are more likely to collect enough data for medical research.

Hassan et al. (2019) note that big data enhances better and more unbiased medical research since there is enough data from which to draw conclusions. In addition, big data in clinical systems can lead to better patient care in that adequate data volumes mean a better understanding of current patient care services. According to Shilo et al. (2020), big data enables healthcare administrators to understand patient care experiences better, thus improving them.

The potential challenges and risks of using big data in clinical systems include privacy and security issues and lack of the required talent/skillset. Big data entails patients’ personal/medical information. Thew (2016) notes that access to patient data by unauthorized persons may lead to privacy and security issues as the data can be used for phishing and scams, among other malicious intentions. In addition, managing and analyzing big data requires a certain skill set, which is a significant challenge. A combination of statistical, medical, and information technology knowledge is needed to apply big data solutions, which is hard to find.

One strategy to mitigate the risks of using big data in clinical systems is robust data privacy and security safeguards. These safeguards include biometric verification, passwords, firewall installation, and the development of institutional policies for data protection. The other strategy is providing comprehensive and quality data training for the personnel to manage big data in an institution. Therefore, using the proposed strategies will help overcome big data risks and enable an institution to enjoy big data benefits.

Discussion: Big Data Risks and Rewards NURS 6051 References

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 1-25. https://doi.org/10.1186/s40537-019-0217-0

Hassan, M. K., El Desouky, A. I., Elghamrawy, S. M., & Sarhan, A. M. (2019). Big data challenges and opportunities in healthcare informatics and smart hospitals. Security in Smart Cities: Models, Applications, and Challenges, 3-26. https://doi.org/10.1007/978-3-030-01560-2_1

Shilo, S., Rossman, H., & Segal, E. (2020). Axes of a revolution: challenges and promises of big data in healthcare. Nature Medicine, 26(1), 29-38. https://doi.org/10.1038/s41591-019-0727-5

Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs. Accessed 21st June 2023 from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs

HealthIT.gov. (2018c). What is an electronic health record (EHR)? Retrieved from 

https://www.healthit.gov/faq/what-electronic-health-record-ehr

Rao-Gupta, S., Kruger, D. Leak, L. D., Tieman, L. A., & Manworren, R. C. B. (2018). Leveraging interactive patient care technology to Improve pain management engagement. Pain Management Nursing, 19(3), 212–221. 

Skiba, D. (2017). Evaluation tools to appraise social media and mobile applications. Informatics, 4(3), 32–40. 

Also Read: Assignment: Evidence-Based Project, Part 2: Advanced Levels of Clinical Inquiry and Systematic Reviews

Discussion: Big Data Risks and Rewards NURS 6051 Example 3

Technology advancements have revolutionized healthcare delivery. Big data entails widely implemented technology, focusing on increasing efficiency in care delivery. An example of big data is electronic health records widely applied in all healthcare facility departments. These technologies convey risks and advantages, and this essay addresses the advantages and rewards of big data in healthcare.

One of the potential benefits of big data is improved decision-making (Ngiam & Khor, 2019). Decisions made are high-quality and big data improves access to information and analytics. Big data helps provide a patient health history and analytics such as prevalence, incidences, and rates without much hustle. The information and analytics help make better care decisions hence better outcomes. 

Ngiam and Khor (2019) note that big data also enhances healthcare data organization, making it easy to retrieve and utilize. Patient information is significant, and big data integration in the healthcare system allows individuals to organize information appropriately for more efficient care. For example, nurses can categorize patients based on age or diagnosis for easier management.

One potential risk of big data use is incompetent data analysis risks (Ngiam & Khor, 2019). Healthcare decisions primarily rely on analyzed data for decision-making. Big data provide an efficient way for data analysis. However, mistakes in the analysis process may lead to significant problems due to poor inferences and bad decisions based on wrong analysis.

Vigilance is thus necessary to prevent such problems. Data privacy issues are also a common problem with Big Data in healthcare (Ngiam & Khor, 2019). External attackers often target data from these systems and can access patient data for personal use, which is against privacy rules and regulations.

Intrusion detection and encryption software are essential strategies for ensuring the security and privacy of healthcare data (Price & Cohen, 2019). These strategies help prevent intruders from accessing the systems and also help ensure intruders who access the systems are detected and action against their activity implemented.

My current institution uses multiple data systems to ensure data analyzed is correct. Analysis using different systems helps establish data consistency and reliability. Big data integral in healthcare systems are vital and covey benefits such as better decision making. However, issues such as privacy and security breaches affect the technologies. Strategies such as intrusion detection can help address these risks.

References

Ngiam, K. Y., & Khor, W. (2019). Big data and machine learning algorithms for healthcare delivery. The Lancet Oncology, 20(5), e262-e273. https://doi.org/10.1016/S1470-2045(19)30149-4

Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37-43. https://doi.org/10.1038/s41591-018-0272-7

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