REPLY TO THE FOLLOWING:
A major application of big data is in the medical field. It has been useful in this area for enhancing services, making medical predictions, decreasing the likelihood of medical errors, boosting medical surveillance, bolstering public health education and making better policy decisions. It’s what made this industry what it is today: stable and reliable (McGonigle & Mastrian, 2022). Consistent quality care at reasonable prices is provided to all patients. There is now a system in place that uses big data as an integral part of clinical operations to make healthcare more like a value-based service.
In the medical field, “big data” refers to the massive amounts of data generated by the widespread adoption of digital methods across the board. Because of technological advancements, more and more big data can be easily generated (Wang et al., 2018). The resulting “big data” is used to enhance the patient experience and includes data from electronic health records, research records, and medical device information.
Healthy patients are the result of big data. It improves monitoring of those with high-risk issues, guaranteeing efficient and individualized treatment plans. Programs centered on the individual patient are made possible by the availability of sufficient data (Dash et al., 2019). It helps medical professionals foresee potential threats to patients’ health and prepare contingency plans to mitigate or eliminate those risks. Through more efficient financial management, big data also helps lower healthcare costs. As a result, hospitals can increase their service capacity while reducing costs (McGonigle & Mastrian, 2022). When it comes to reducing the likelihood of mistakes, big data is useful as well. It allows for the development of informational systems that aid medical professionals in avoiding the kind of errors that result in the administration of the incorrect medication. Automatic patient data transfers and transparent individual records aid in the prevention of mistakes.
Strategic planning is aided by big data, which allows for the analysis of records and checkup results to better understand major diseases and the reasons why people would or would not take treatments. Big data can also help institutions strengthen security and reduce fraud. Big data’s data analytics are useful for lowering this evil because they make it possible to foresee potential cyber-attacks (McGonigle & Mastrian, 2022).
The healthcare industry is also vulnerable due to the risks presented by big data. Issues with data governance are a potential stumbling block, as this could lead to sensitive data falling into the wrong hands. If information is sent to the wrong place, it could be used against patients and the institution. Information about an organization’s finances, for instance, could be used against it if it fell into the wrong hands (McGonigle & Mastrian, 2022). However, by using data science experts to guarantee expert data transfer and handling, this problem can be fixed or avoided altogether.
FYI you must used 2 of the following as Resources but the reply requires 3:
- McGonigle, D., & Mastrian, K. G. (2022).Nursing informatics and the foundation of knowledge(5th ed.). Jones & Bartlett Learning.
- Chapter 22, â€œData Mining as a Research Toolâ€ (pp. 537-558)
- Chapter 24, â€œBioinformatics, Biomedical Informatics, and Computational Biologyâ€ (pp. 581-588)
- Glassman, K. S. (2017). Using data in nursing practice Links to an external site.. American Nurse Today, 12(11), 45â€“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 Links to an external site.. 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 Links to an external site.. Technological Forecasting and Social Change, 126(1), 3â€“13.