Assignent Solutionnurs

Respond to each of the following two colleagues by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks. A minimum of 200 words is required for each answer. Use at least 2 current resources. 1) Big Data (by Leigh R.)
Healthcare workers use Big Data daily. Big Data consists of specifics obtained through technology that are reviewed and compiled to form patterns associated with an individual. In healthcare, Big Data can assume many forms. Providers use big data to trend patients’ disease processes, determine whether treatments are working, and predict future problems.
Big Data Use During Covid Big Data use has been crucial during the Covid Pandemic. Per Khamisy-Farah et al. (2021), Big Data also plays a vital role in maintaining currency with continuing education during the covid pandemic. One of the most recent positive uses of Big Data is to trend Covid-19 in specific and surrounding areas. This data has been beneficial in preparing communities for uptake in the virus locally and allowing society to arrange for necessities if they choose to self-quarantine.
The government has utilized Big Data throughout the Covid-19 pandemic to track the effects of social distancing. Mahmoudi and Xiong (2022) state that collected data on Covid-19 has been beneficial in decreasing Covid-19 transfer and mortality. After data has been collected, it is reported to the news sites to allow knowledge to be easily accessible to the public. It is up to the individual to decide to limit public interaction if Covid has increased in their area and self-monitor for any possible symptoms to prevent the spread of Covid.
Big Data Proposal
During the beginning of Covid-19, data was not readily available to track the spread of Covid. This created many issues with supply limitations to the regions that were hit the hardest. Many hospitals dealt with inadequate blood supply and not enough personal protective equipment (PPE) for staff. Most facilities had to prioritize ventilators and high-flow distribution to those patients with the best chance of survival.
To better prepare for the onslaught of Covid-19 cases, hospital systems should utilize Big Data information to obtain equipment that will likely be needed. Cuvero et al. (2021) propose using a three-phase program for preparedness. The three parts of this model are the proactive, concurrent, and reactive segments. I suggest employing the collected Big Data information and restocking all supplies before important health events. This has been used to some extent, but hospitals should borrow crucial equipment from facilities at a decreased risk for immediate Covid assault to prepare further. If this is done correctly, facilities will not run the risk of not having enough equipment to care for their patients.
If Big Data predicts the northwest will be hit first with new deadly strains, the equipment could be borrowed from several regions east to prepare. This borrowing system should continue in the same fashion as the wave crosses the United States. It would mean that equipment would be borrowed from the western states once the strain enters the southeast. Borrowing from several regions away should allow ample time for each area to get through the time of highest infection rates. To facilitate this process, specific hospital systems should collaborate to borrow from one another.
Risks of Utilizing Big Data
Sometimes we depend on Big Data an excessive amount to predict the future. Due to the varying levels of data, some technology might not be able to analyze the data (Leung and Zhao 2021) precisely. Data analysis must be thoroughly examined for accuracy before distribution. Hospital systems should take extra care to ensure proper equipment and techniques are utilized to analyze Big Data before affecting patient care. Take vital sign data; if appropriate methods are not in place to compile and trend one’s vital signs, a patient could accrue unnecessary nights or treatment during hospitalization.
References
Cuvero, M., Pilkington, A., & Barnes, D. (2021). Supply Chain Management and Resilience During Disruption. Evaluation of the Covid-19 Pandemic on the Supply of Personal Protective Equipment. 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Industrial Engineering and Engineering Management (IEEM), 2021 IEEE International Conference On, 233–237. https://doi.org/10.1109/IEEM50564.2021.9672913
Khamisy-Farah, R., Gilbey, P., Furstenau, L. B., Sott, M. K., Farah, R., Viviani, M., Bisogni, M., Kong, J. D., Ciliberti, R., & Bragazzi, N. L. (2021). Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature. International Journal of Environmental Research and Public Health, 18(17). https://doi.org/10.3390/ijerph18178989
Leung, C. K., & Zhao, C. (2021). Big Data Intelligence Solution for Health Analytics of COVID-19 Data with Spatial Hierarchy. 2021 IEEE 7th International Conference on Big Data Intelligence and Computing (DataCom), Big Data Intelligence and Computing (DataCom), 2021 IEEE 7th International Conference on, DATACOM, 13–20. https://doi.org/10.1109/DataCom53700.2021.00009
Mahmoudi, J., & Xiong, C. (2022). How social distancing, mobility, and preventive policies affect COVID-19 outcomes: Big data-driven evidence from the District of Columbia-Maryland-Virginia (DMV) megaregion. PloS One, 17(2), e0263820. https://doi.org/10.1371/journal.pone.0263820 2) Benefit of Using Big Data (by Nazm H)
Big data can be very useful to the healthcare world. Big data can create prescriptive analysis and with the newly gathered knowledge, suggest interventions for an upcoming event. (Thew, 2016). Using big data will benefit the population on a large scale, in regards to risk management and disease control. (2016). With the evolution of data mining, healthcare organizations can have access to data in real time. (McGonigle & Mastrian, 2022). CNE’s will be able to have real time data, as opposed to having to wait for end of the year or quarterly reports in order to make decisions. (2016). In other words, CNE’s can make real time decisions based on the data collected and spend less time waiting for different data to come in, to make an executive decision.
Risk of Using Big Data
Even with the big data gathering and the information available, there are some challenges that occur in the process. The setback of using big data is some of the unmeasurable issues such as patient compliance, nursing competency, and commitment by the nurses to execute interventions accordingly and as necessary to not interfere with data results. (Thew, 2016). Things like patient compliance are a major challenge due to the amount of time and effort put into the data gathering and transforming it into knowledge and educating the patient simply for them to not listen or follow the treatment plan. The informatics model process of Data-Information-Knowledge-Wisdom model, consists of collecting data, transforming that data into information and knowledge, then turning that knowledge into wisdom (Walden, 2018). Using that model, nurses educate their patients and attempt at increasing prognosis, so when patients become noncompliant, it is a waste of resources and time for the healthcare organization as well as the nursing staff. Proposing a Strategy
Patient compliance is an issue nurses and other healthcare professionals deal with on a daily basis. Regardless of the effort nurses put into care plans, proper medication administration, therapeutic interaction and communication between nurse and patient, and patient education upon discharge, some patients will simply be non-compliant. Non-compliance can be for several reasons, poor time management due to life, work, children etc, or patients have a medical diagnosis such as dementia that prevents them from being able to execute needed tasks or take medications on time due to memory problems. In my experience, compliance can be impacted with therapeutic communication approach. When one method of communication doesn’t work, nurses can attempt a different route, whether that be reminding the patient to set treatment goals, redirecting the patient if they are upset, offering a spiritual consultation from a religious expert, or simply phoning a family member for moral support. These are several methods that nurses use on the floor with their patients when a patient is being uncooperative or isn’t compliant with the care plans. References
McGonigle, D. & Mastrian, K.G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Barlett Learning. Thew, J. (2016). Big data means big potential, challenges for nurse execs. Retrieved from:https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs
Walden University, LLC. (Producer). (2018). Data-Information-Knowledge-Wisdom [Video file]. Baltimore, MD: Author. Learning Resources
Required Readings
McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.
Chapter 22, “Data Mining as a Research Tool” (pp. 537-558)
Chapter 24, “Bioinformatics, Biomedical Informatics, and Computational Biology” (pp. 581-588)
Glassman, K. S. (2017). Using data in nursing practice. American Nurse Today, 12(11), 45–47. Retrieved from https://www.americannursetoday.com/wp-content/uplo…Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs. Retrieved from https://www.healthleadersmedia.com/nursing/big-dat…
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
Walden University, LLC. (Executive Producer). (2012). Data, information, knowledge and wisdom continuum [Multimedia file]. Baltimore, MD: Author. Retrieved from http://cdn-media.waldenu.edu/2dett4d/Walden/NURS/6…Walden University, LLC. (Producer). (2018). Health Informatics and Population Health: Analyzing Data for Clinical Success [Video file]. Baltimore, MD: Author.
Accessible player –Downloads–Download Video w/CCDownload AudioDownload TranscriptVinay Shanthagiri. (2014). Big Data in Health Informatics [Video file]. Retrieved from