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NURS 6051 Week 5 Discussion: Big Data Risks and Rewards

NURS 6051 Week 5 Discussion: Big Data Risks and Rewards NURS 6051 Week 5 Discussion: Big Data Risks and RewardsDiscussion: Big Data Risks and RewardsWhen 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.NURS 6051 Week 5 Discussion: Big Data Risks and RewardsAs 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.ORDER A PLAGIARISM-FREE PAPER HERETo 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.NURS 6051 Week 5 Discussion: Big Data Risks and RewardsBy 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.*Note: Throughout this program, your fellow students are referred to as colleagues. Rubric Detail Select Grid View or List View to change the rubric’s layout.ContentName: NURS_5051_Module03_Week05_Discussion_RubricNURS 6051 Week 5 Discussion: Big Data Risks and RewardsGrid ViewList ViewExcellentGoodFairPoorMain PostingPoints Range: 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.NURS 6051 Week 5 Discussion: Big Data Risks and RewardsSupported 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.Points Range: 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.NURS 6051 Week 5 Discussion: Big Data Risks and RewardsPoints Range: 35 (35%) – 39 (39%)Responds to some of the discussion question(s). One or two criteria are not addressed or are superficially addressed.NURS 6051 Week 5 Discussion: Big Data Risks and RewardsIs 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.Points Range: 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.Main Post: TimelinessPoints Range: 10 (10%) – 10 (10%)Posts main post by day 3.Points Range: 0 (0%) – 0 (0%)Points Range: 0 (0%) – 0 (0%)Points Range: 0 (0%) – 0 (0%)Does not post by day 3.First ResponsePoints Range: 17 (17%) – 18 (18%)Response exhibits synthesis, critical thinking, and application to practice settings. Responds fully to questions posed by faculty. Provides clear, concise opinions and ideas that are supported by at least two scholarly sources.NURS 6051 Week 5 Discussion: Big Data Risks and RewardsDemonstrates synthesis and understanding of learning objectives.NURS 6051 Week 5 Discussion: Big Data Risks and RewardsCommunication is professional and respectful to colleagues. Responses to faculty questions are fully answered, if posed. Response is effectively written in standard, edited English.Points Range: 15 (15%) – 16 (16%)Response exhibits critical thinking and application to practice settings. Communication is professional and respectful to colleagues. Responses to faculty questions are answered, if posed. Provides clear, concise opinions and ideas that are supported by two or more credible sources. Response is effectively written in standard, edited English.Points Range: 13 (13%) – 14 (14%)Response is on topic and may have some depth. Responses posted in the discussion may lack effective professional communication. Responses to faculty questions are somewhat answered, if posed. Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited.Points Range: 0 (0%) – 12 (12%)Response may not be on topic and lacks depth. Responses posted in the discussion lack effective professional communication. Responses to faculty questions are missing. No credible sources are cited.Second ResponsePoints Range: 16 (16%) – 17 (17%)Response exhibits synthesis, critical thinking, and application to practice settings. Responds fully to questions posed by faculty. Provides clear, concise opinions and ideas that are supported by at least two scholarly sources.NURS 6051 Week 5 Discussion: Big Data Risks and RewardsDemonstrates synthesis and understanding of learning objectives. Communication is professional and respectful to colleagues. Responses to faculty questions are fully answered, if posed.NURS 6051 Week 5 Discussion: Big Data Risks and RewardsResponse is effectively written in standard, edited English.Points Range: 14 (14%) – 15 (15%)Response exhibits critical thinking and application to practice settings.NURS 6051 Week 5 Discussion: Big Data Risks and RewardsCommunication is professional and respectful to colleagues. Responses to faculty questions are answered, if posed. Provides clear, concise opinions and ideas that are supported by two or more credible sources. Response is effectively written in standard, edited English.Points Range: 12 (12%) – 13 (13%)Response is on topic and may have some depth. Responses posted in the discussion may lack effective professional communication. Responses to faculty questions are somewhat answered, if posed. Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited.Points Range: 0 (0%) – 11 (11%)Response may not be on topic and lacks depth. Responses posted in the discussion lack effective professional communication. Responses to faculty questions are missing. No credible sources are cited.ParticipationPoints Range: 5 (5%) – 5 (5%)Meets requirements for participation by posting on three different days.Points Range: 0 (0%) – 0 (0%)Points Range: 0 (0%) – 0 (0%)Points Range: 0 (0%) – 0 (0%)Does not meet requirements for participation by posting on 3 different days. Total Points: 100Name: NURS_5051_Module03_Week05_Discussion_RubricDiscussion – Week 5CollapseNURS 6051 Week 5 Discussion: Big Data Risks and RewardsBig Data Risks and RewardsWhen 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:NURS 6051 Week 5 Discussion: Big Data Risks and RewardsReview 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 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.Click on the Reply button below to reveal the textbox for entering your message. Then click on the Submit button to post your message.NURS 6051 Week 5 Discussion: Big Data Risks and Rewards*Note: Throughout this program, your fellow students are referred to as colleagues.DiscussionThe healthcare industry faces multiple challenges, ranging from new disease outbreaks to maintaining an optimal operational efficiency (Choudhury, 2018). Big data has the potential to help with challenges related to healthcare. “Big data in healthcare” refers to the abundant health data amassed from numerous sources including electronic health records (EHRs), medical imaging, genomic sequencing, payor records, pharmaceutical research, wearables, and medical devices (Healthcare Big Data and the Promise of Value-Based Care, 2018). By digitizing, combining and effectively using big data, healthcare organizations ranging from single-physician offices and multi-provider groups to large hospital networks and accountable care organizations stand to realize significant benefits (Burghard, 2012). Some such benefits are the detection of diseases at earlier stages when they can be more easily treated as well as managing specific individual and detecting health care fraud more quickly.NURS 6051 Week 5 Discussion: Big Data Risks and RewardsReferencesBurghard, C. (2012) Big data and analytics key to accountable care success. Choudhury, A. (2018, February 7). Top benefits of big data in the healthcare industry. Business Wire.Healthcare Big Data and the Promise of Value-Based Care. (2018, January 1). NEJM Catalyst. https://catalyst.nejm.org/doi/full/10.1056/CAT.18.0DiscussionThe healthcare industry faces multiple challenges, ranging from new disease outbreaks to maintaining an optimal operational efficiency (Choudhury, 2018). Big data analytics can help in solving these healthcare challenges. With the vast amount of data available in the healthcare sector like financial, clinical, R&D, administration and operational data, big data can derive meaningful insights to improve the operational efficiency of the industry. “Big data in healthcare” refers to the abundant health data amassed from numerous sources including electronic health records (EHRs), medical imaging, genomic sequencing, payor records, pharmaceutical research, wearables, and medical devices (Healthcare Big Data and the Promise of Value-Based Care, 2018).NURS 6051 Week 5 Discussion: Big Data Risks and Rewards                                                                                                                                     ReferencesChoudhury, A. (2018, February 7). Top benefits of big data in the healthcare industry. Business Wire. https://www.businesswire.com/news/home/20180207005640/en/Top-Benefits-Big-Data-Healthcare-Industry-Quantzig#:~:text=1%20Advanced%20patient%20care%3A%20Electronic%20health%20records%20help,benefits%20of%20big%20data%20in%20the%20healthcare%20industry.Healthcare Big Data and the Promise of Value-Based Care. (2018, January 1). NEJM Catalyst. https://catalyst.nejm.org/doi/full/10.1056/CAT.18.0290RE: Discussion – Week 5CollapseThe Affordable Care Act (ACA) has radically changed the healthcare landscape. The rise of EHR software as a critical tool for the delivery and continuity of care was one of its outcomes. EHR helps provides better care for their patients by enabling quick access to patient records, resulting in more efficient care. They also help in treatment effectiveness while increasing the practice’s operational efficiency.NURS 6051 Week 5 Discussion: Big Data Risks and RewardsOn the care provider’s side, most EHR provide physicians with health analytics that help recognize patterns, predict diagnosis and recommend potential treatment options. These analytics lead to more successful overall patient results the first time, rather than simply relying on trial-and-error methods. On the patient’s side, they have access to patient portals, which give them access to historical medical information such as lab and imaging results, medications, diagnoses, and more. Patients can communicate with their physicians by sharing notes, exchanging instant messages, and even video calling. Both physicians and patients can use the portal to track an individual’s treatment progress. This also makes preventive care easier.NURS 6051 Week 5 Discussion: Big Data Risks and Rewards References:Jennifer L Hall, John J Ryan, Bruce E Bray, Candice Brown, David Lanfear, L Kristin Newby, Mary V Relling, Neil J Risch, Dan M Roden, Stanley Y Shaw. Merging electronic health record data and genomics for cardiovascular research a science advisory from the american heart association. Circulation: Cardiovascular Genetics. 2016;9(2):193–202.National Institute of Health. 2017. https://www.nih.gov/research-training/allofus-research-programPatel MB, Jackson JC, Morandi A, et al: Incidence and risk factors for intensive care unit-related post-traumatic stress disorder in veterans and civilians. Am J Respir Crit Care Med 2016; 193:1373–1381RE: Discussion – Week 5Collapse            Big data is ever-changing to accommodate enhanced technology. As far as healthcare, the innovative ideas of IT and nursing collaborations make it easier for physicians and healthcare workers alike in their workflow. Although, within certain titles, as I experienced today, some doctors are not aware of how to discharge patients and prescribe meds to pharmacies because their PAs do the bulk of that work. Although I am not a PA, I was able to walk him through the process or that operational efficiency that you spoke of.NURS 6051 Week 5 Discussion: Big Data Risks and RewardsIn analytics, a huge amount of data is used on computer networks that lead to analyzing suspicious activity (Sudar, 2021). As such case, a little deviation from the healthcare world, I received an email from Wegman’s which is a local grocery store that there was a breach in two of their cloud databases that exposed shoppers club numbers, addresses, and individual information for people who have accounts with them. They advised us that the issue was handled, hired an outside firm to detect where the breach happened, and encouraged us to change our passwords. In healthcare and retail, “Big data analytics is not focused on detecting fraud” (B. K. Jha, 2020, p. 267). If the focus on preventing fraud is not very existent, I wonder why so much of our society is totally dependant on a digitized world? ReferencesB. K. Jha, G. G. (2020). Fraud Detection and Prevention by using Big Data Analytics. Fourth International Conference on Computing Methodologies and Communication, (pp. 267-274). doi: 10.1109/ICCMC48092.2020.ICCMC-00050NURS 6051 Week 5 Discussion: Big Data Risks and RewardsSudar, K. M. (2021). Analysis of Intruder Detection in Big Data Analytics. International Conference on Computer Communication and Informatics, (pp. 1-5). https://doi-org.ezp.waldenulibrary.org/10.1109/ICCCI50826.2021.9402402RE: Discussion – Week 5CollapseHello! Great post this week! Big data has proven to be one of the most essential factors of progressive treatment in healthcare. With the aid of big volumes of data, we as a healthcare team are able to compare former cases and treatment options to deem them viable for any present cases. However, with these large volumes of data, a big issue some health care systems may face include a breach of information and exposure of a patient’s personal information to unnecessary parties (Pastorino et al., 2020). 60% of the general population in the United States and 80% of healthcare facilities have no adequate awareness of strategies to shield electronic devices from malware (Ienca et al., 2018). Hence, there is a great risk for hackers to access private information. Building secure encrypted systems can be complemented by evaluating and updating system firewalls to minimize brute force attacks to archived patient’s electronic data (Mcgonigle & Mastrian, 2017, pp. 537–551).NURS 6051 Week 5 Discussion: Big Data Risks and Rewards References 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), 204–237. https://doi.org/10.1371/journal.pone.0204937Mcgonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed., Vol. 2, pp. 537–551). Jones & Bartlett Learning.Pastorino, R., Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., & Boccia, S. (2020). 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/ckz168NURS 6051 Week 5 Discussion: Big Data Risks and Rewards

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