Posted: September 23rd, 2022

Big Data Risks and Rewards NURS 6051

Big Data Risks and Rewards NURS 6051

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. Big Data Risks and Rewards NURS 6051

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 for 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.
By Day 3 of Week 5 of Big Data Risks and Rewards NURS 6051
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. Big Data Risks and Rewards NURS 6051

By Day 6 of Week 5 of Big Data Risks and Rewards NURS 6051
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.

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

Submission and Grading Information

Grading Criteria

Also Read:

Policy/Regulation Fact Sheet NURS 6051

Healthcare Information Technology Trends NURS 6051

NURS 6051 Discussion: Interaction Between Nurse Informaticists and Other Specialists

Big Data Risks and Rewards NURS 6051 Week 5 Discussion Resources
Required Readings

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)
Glassman, K. S. (2017). Using data in nursing practice. American Nurse Today, 12(11), 45–47. Retrieved from https://www.americannursetoday.com/wp-content/uploads/2017/11/ant11-Data-1030.pdf

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

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.

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. Big Data Risks and Rewards NURS 6051

Accessible player –Downloads– Download Video w/CC Download Audio Download Transcript

Vinay Shanthagiri. (2014). Big Data in Health Informatics [Video file]. Retrieved from https://www.youtube.com/watch?v=4W6zGmH_pOw

One potential benefit of using big data as part of a clinical system is the ability to identify patterns and trends that can inform clinical decision-making. With large amounts of data, healthcare professionals can use data analytics to identify patterns that may not be easily visible otherwise. For example, a healthcare system may use big data to identify patients who are at a high risk of readmission to the hospital based on factors such as age, comorbidities, and social determinants of health. This can allow for proactive interventions to reduce readmissions and improve patient outcomes.

One potential challenge or risk of using big data as part of a clinical system is the potential for breaches of privacy and security. With large amounts of sensitive data being stored and analyzed, there is a risk that this information may be accessed by unauthorized individuals or entities. This can lead to a breach of patient confidentiality and potentially harm patients. Additionally, there is a risk of data manipulation or errors, which can lead to inaccurate clinical decision-making.

One strategy to mitigate these risks is to implement robust security protocols and data governance policies. This can include encryption of sensitive data, access controls, and regular security audits. It is also important to have trained staff who are knowledgeable about data security and privacy regulations. Additionally, implementing data quality assurance processes can help ensure the accuracy and reliability of the data being used for analysis. Overall, a comprehensive approach to data governance and security can help mitigate the risks associated with using big data in clinical systems.

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