Discussion: Big Data Risks and Rewards

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Big Data Risks and Rewards Comprehensive Discussion Essay Example

NURS 6051 Discussion: 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.

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.

RE: Main Discussion Post- Week 5Collapse

Most of us live in a connected to the world through cellphones, social media, computers, game platforms, and more.  That connection seems never to break, even when at work as we carry our phones with us and log to computers. We also help connect patients to database banks, even when they do it even realize it.   We live in a world of big data and that data is priceless.  It comes with positive outcomes and at times, it can also have adverse effects.

There are possibly countless benefits of big data in the healthcare system, and nurses are the ones responsible for entering most of that data.  From the second we get to work and login to the electronic health record (EHR) to the moment we log off, we enter valuable information into computers.  That data can be used to develop better protocols, enhance patient safety, better patient outcomes, even ease our nursing profession, and much more.

According to an article published by Health Information Science and System, some of the benefits of synthesizing and analyzing big data are:

The development of more thorough and insightful diagnoses and treatments which could result in higher quality care by analyzing patterns and trends; monitor adherence to drug and treatment regimens and detect trends that lead to individual and population wellness; detecting diseases at earlier stages; reducing readmissions by identifying environmental or lifestyle factors that increase risk or trigger adverse events; adjusting treatment plans accordingly; improving outcomes by examining vitals from at-home health monitors; managing population health by detecting vulnerabilities within patient populations during disease outbreaks or disasters; and bringing clinical, financial and operational data together to analyze resource utilization productively and in real-time. (W. Raghupathi & V. Raghupathi, 2014)

Some challenges have been found along the way, such as the inability to fully implement standardized nursing terminology (SNT), which, if addressed, can improve data analysis.  “The use of standardized nursing terminologies (SNTs) to document nursing care enables the easy retrieval and analysis of nursing data while also representing the nurse’s clinical reasoning” (Macieira et al., 2017).  SNTs would better communication among nurses and providers, increase the visibility of nursing interventions, improve patient care, and facilitate nursing assessment competency (Rutherford, 2008).

“The lack of data standardization can also make it challenging for a CNE to assess how the organization or a particular unit is performing and to make well-informed decisions about what to change” (Thew, 2018). “Englelbright says that by breaking down data silos, big data will also facilitate a balanced approach to assessing organizational and nursing performance” (Thew, 2018).

As we have discussed and learned throughout this course, nursing informatics and big data, helping our professions and patients, but all these benefits also come with many challenges as well.

What are the odds we get to talk during this week about ‘Big Data’ and its benefits and challenges a week after Hackensack Meridian Health, New Jersey’s largest hospital system experienced a ransomware cyber-attack?  Although no patient medical record was reported stolen, personal and financial information, including healthcare insurance data, was stolen.  To regain control over its systems, Hackensack Meridian was forced to pay an undisclosed amount of money in ransom (Eddy, 2019).  Having lived in New York City for years, I knew Hackensack was a large hospital system. Still, I was not aware it operated a total of 17 facilities, which includes acute care centers to nursing homes and rehabilitation centers.  “The attack forced hospitals to reschedule nonemergency surgeries and doctors and nurses to deliver care without access to electronic records” (The Associated Press, 2019a).  These types of cyber-attacks targeting healthcare facilities are more common than we think.  On October 2, 2019, an Alabama hospital system was a victim of a ransomware attack. As reported, during the cyber-attack, the hospitals involved quit accepting new patients.  “The Tuscaloosa News quoted spokesman Brad Fisher as saying the hospital system paid the attackers” (The Associated Press, 2019b). A quick Google search provided over a dozen healthcare facility cyber-attacks in recent years in which patient personal, financial, healthcare insurance, and healthcare records were stolen.

During these cyber-attacks, the same computers and systems meant to assist our patients were locked and highjacked for ransom. Although the reports mentioned no patient health record was exposed, the investigation at Hackensack is still ongoing.  These cyber-attacks have exposed the vulnerability of a system we usually do not associate with cyber-crimes.  When think of data breach, banks, government offices, and credit bureaus such as Trans-Union and Equifax come to our minds, not Hackensack Meridian, Mount Sinai, or Swedish Health.

Big data threat is not limited to cyber-attacks, but also internal data mishandling. “One-quarter of all the cases [of healthcare data breaches] were caused by unauthorized access or disclosure – more than twice the amount that was caused by external hackers” (Brooks & Jiang, 2018). Sometimes the data is mistakenly shared with the wrong recipients by hospitals, doctors, pharmacies, and even health insurance companies as not all facilities have strict regulations.

When I think about privacy in healthcare, I initially think of patient privacy and the Health Insurance Portability and Accountability Act (HIPPA).  We put all that data and not always know who has access to it.  How do we know this data is truly kept private when so many agencies, organizations, and analytic companies have access to it?  Who keeps track of what is shared, how it is used, and what is used for?  We live ina society where personal data and our digital footprint is worth billions of dollars to companies that want to influence us.  How do we ensure patient data does not fall in the hands of them?

References

Brooks, C. & Jiang, X. (2018, November 16). Health care providers – not hackers – leak more of your data. Retrieved from https://msutoday.msu.edu/news/2018/health-care-providers-not-hackers-leak-more-of-your-data/

Eddy, N. (2019, December 16). Hackensack Meridian Health pays up after ransomware attack. Retrieved from https://www.healthcareitnews.com/news/hackensack-meridian-health-pays-after-ransomware-attack

Macieira, T., 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/

Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems,2(3). doi:10.1186/2047-2501-2-3

Rutherford, M. A. (2008). Standardized nursing language: What does it mean for nursing practice? Online Journal of Issues in Nursing, 13(1), 1–12. https://doi.org/10.3912/OJIN.Vol13No01PPT05

The Associated Press. (2019a, December 13). Large hospital system says it was hit by ransomware attack. ABC News. Retrieved from https://abcnews.go.com/Health/wireStory/large-hospital-system-hit-ransomware-attack-67724061

The Associated Press. (2019b, October 5). Report: Alabama hospitals pay hackers in ransomware attack. ABC News. Retrieved from https://abcnews.go.com/Technology/wireStory/report-alabama-hospitals-pay-hackers-ransomware-attack-66084508

Thew, J. (2016, April 19). Big data means big potential, challenges for nurses execs. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs

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.

By Day 6 of Week 4

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.

Big Data Risks and Rewards

Big data refers to a large and complex set of data that, when examined as a wholly integrated data, yields essential information than a small unintegrated collection of data. In the contemporary world, big data is increasingly becoming more prevalent, impacting nursing in various ways. Big data offers a nursing system a considerable opportunity to advance the vision of promoting human health and well-being. Although big data analytics is riddled with challenges, it is useful in decision-making in clinical systems.

Big data is a promising breakthrough in health care decision making. Big data analytics in the context of nursing enable an organization to analyze large volumes and velocity of data from various nursing networks to aid in evidence-based decision making and action (Macieira et al., 2017). It allows the integration of clinical information that provides health care insights to help nurses meet patients’ needs and improve the quality of healthcare. Moreover, big data can be used to understand the impact of nursing care and to expand the responsibility to meet continuous emerging needs.

Big data allows clinical systems to realize informatics benefits, including improved quality and accuracy of clinical decision and instant access to vital health records and information. Additionally, big data is a potential source for managerial benefits which allow health care organization to monitor and monitor the firm’s resources and evaluate the operation and support strategic business decisions (Wang, Kung & Byrd, 2018). Furthermore, big data analytics gives the clinical system the capability to generate accurate data and make predictions based on new observations. Predictive analytics play a crucial role in the clinical order of reducing uncertainty and preventing readmissions

Lack of substantial experience in big data analytics is one of the most significant challenges impeding the realization of maximum big data benefits. Evidence indicates that only a few percentages of health care organizations have the capability to conduct rigorous big data analytics to aid in the decision-making process (Wang et al., 2018). The lack of understanding by clinical officers on the value of big data analytic in the clinical system compounds this challenge. Therefore, the clinical system can leverage big data analytics as a means of transforming nursing in the era of informatics.

References NURS 6051 Discussion: Big Data Risks and Rewards

Macieira, T. G., Smith, M. B., Davis, N., Yao, Y., Wilkie, D. J., Lopez, K. D., & Keenan, G. (2018). Evidence of progress in making nursing practice visible using standardized nursing data: A systematic review. AMIA Annual Symposium Proceedings Archive, 1205–1214. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977718/

Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change126, 3-13. doi:10.1016/j.techfore.2015.12.019

Thank you for your articulate post. As you explained big data can be used to understand the impact of nursing care and expand the responsibility to meet continuous emerging needs. Big data can be used in a variety of industries. The top benefits of using big data in healthcare include advancing patient care, improving operational efficiency, finding cures for diseases (Business Wire, 2018). An example of improving operational efficiency would be running reports on readmissions of COPD exacerbations. With this data, a company can examine the admission rates while analyzing staff efficiency. Predictive analytics can be used on the COPD patient to understand key factors in readmissions.

As nurses, we understand that healthcare is continually changing. The same principals can be applied to big data. One of the biggest problems with big data is that “it grows constantly, and organizations often fail to capture the opportunities and extract actionable data” (Joshi, 2018). Because of this, we may miss opportunities to best serve our patients.

References

Business Wire. (2018). Top Benefits of Big Data in the Healthcare Industry. Retrieved from https://www.businesswire.com/news/home/20180207005640/en/Top-Benefits-Big-Data-Healthcare-Industry-Quantzig

Joshi, N. (2018). Problems with Big Data that we failed to notice. Retrieved from https://www.allerin.com/blog/problems-with-big-data-that-we-failed-to-notice

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Discussion: Big Data Risks and Rewards
Discussion: Big Data Risks and Rewards

Cybercrime in healthcare indeed puts a patient’s health and privacy at risk. “It is one of the most targeted sectors globally; 81% of 223 organizations surveyed, and >110 million patients in the US had their data compromised in 2015 alone” (Martin, Martin, Hankin, Darzi, & Kinross, 2017, para. 3). We often think about cybercrime in terms of a virus or spyware stealing information, or money, however, cybercriminals are always coming up with new schemes. For example, in 2016 The Hollywood Presbyterian Medical Center’s entire computer system was essentially hijacked for ransom, “shut down its network for ten days, preventing staff from accessing medical records or using medical equipment until the hospital paid the ransom” (Martin et al., 2017, para. 6).

The increased use of nursing informaticists in the healthcare system is a step in the right direction for hospitals as they continue to fight cybercrime while expanding the use of big data. The article, Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations, finds that while hospital systems are investing a large number of finances into big data, much of its capabilities are still underutilized (Wang, Kung, & Byrd, 2018). As hospital systems increase the usage and find new applications for big data, opportunities for cybercrime will increase, and informaticists will have to be ever vigilant in their protection of patient privacy.

Martin, G., Martin, P., Hankin, C., Darzi, A., & Kinross, J. (2017, July 6, 2017). Cybersecurity and healthcare: How safe are we? thebmj358. http://dx.doi.org/ 10.1136/bmj.j3179

Wang, Y., Kung, L., & Byrd, T. A. (2018, January 2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. ScienceDirect126(), 3-13. http://dx.doi.org/https://doi.org/10.1016/j.techfore.2015.12.019

Rubric Detail

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Content
Name: NURS_5051_Module03_Week05_Discussion_Rubric

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Excellent Good Fair Poor
Main Posting
Points 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.

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.

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.

Points Range: 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.

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: Timeliness
Points Range: 10 (10%) – 10 (10%)
Posts main post by day 3.

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Discussion: Big Data Risks and Rewards
Discussion: Big Data Risks and Rewards

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 Response
Points 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.

Demonstrates synthesis and understanding of learning objectives.

Communication 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 Response
Points 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.

Demonstrates synthesis and understanding of learning objectives.

Communication 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: 14 (14%) – 15 (15%)
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: 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.

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Discussion: Big Data Risks and Rewards
Discussion: Big Data Risks and Rewards

No credible sources are cited.
Participation
Points 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: 100
Name: NURS_5051_Module03_Week05_Discussion_RubricRE: Discussion – Week 5Collapse

Big data affects many aspects of life within a clinical system and outside of it. There are several benefits to the use of big data, one being cost reduction. When speaking of cost reduction, one hospital in England uses analytics to predict admissions over a period, helping hospital systems assign staff based on the needs predicted. This ensures there are no overstaffing issues, increases efficiency, and decreases wait times. (Berke, 2020). Some other benefits are follow-up care, “current” care, and medication error prevention.

One challenge to the use of big data as a part of any clinical system is that it can be overwhelming. Nurse leaders are drowning in data given to them and it is hard to differentiate and analyze it all. Looking at one unit specifically is overwhelming let alone a whole hospital system. There are many different aspects to keep a unit running and having everything in sync all at the same time is rare. A lot of time when dealing with big data the chief nurse executives must look at the big picture for the business side of things and not everything that matters to them most is included in that, and they would then have to fight for it on the side. (HealthLeaders, n.d.). Throughout my research, another risk I saw was sharing patient data within HIPPA guidelines as well as data privacy and regulations issues. Like everything else, there are challenges that go along with any benefit.

One strategy to start with to help with the overwhelming amount of data from many different sources is to try and have a hospital system as a whole use one system. I know in my experience at my hospital the nurses use one system, the doctors use a different system for some things, radiology uses their own system, and dietary uses a different system. All these different systems do not communicate with each other so when attempting to collect data from many different places it is not always the most accurate. It is difficult to navigate and when there is not good, accurate data being analyzed it is problematic to implement the appropriate, productive changes that need to be made to a hospital system. A simpler, more realistic strategy would be data mining projects. Data mining uses a four-phase process to produce a solution: Problem identification, exploration of data, pattern discovery, and knowledge deployment. (McGonigle & Mastrian, 2022). This will help predict trends that can increase the productivity of changes made to accommodate future needs.

References:

HealthLeaders. (n.d.). Big data means big potential, challenges for nurse execs. HealthLeaders Media. Retrieved December 30, 2021, from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs

Lindsey Berke. Dimensional Insight. (n.d.). Retrieved December 30, 2021, from https://www.dimins.com/blog/2020/03/02/big-data-healthcare/#:~:text=Top%20Advantages%20of%20Big%20Data%20in%20The%20Healthcare,many%20times%20there%20are%20lives%20on%20the%20line.

McGonigle, D., & Mastrian, K. (2022). Nursing Informatics and the foundation of knowledge. Jones et Bartlett Learning.

Big Data Risks and Rewards Comprehensive Discussion Essay Example
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