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.
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/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
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/6051/03/mm/continuum/index.html
Walden University, LLC. (Producer). (2018). Health Informatics and Population Health: Analyzing Data for Clinical Success [Video file]. Baltimore, MD: Author.
Karin Banks
Jun 23, 2024 at 9:48 PM
Triage Nursing Using Big Data
A significant amount of data is utilized by healthcare professionals to customize medical treatments for individual patients and enhance outcomes. The Precision Medicine Initiative aims to collect 1 million patient data sets while ensuring anonymity. Healthcare systems employ predictive models to assess each patient’s risk of various medical issues. Precision nursing leverages big data and analytics to identify high-risk patients and enhance care (The Promise of Big Data: Improving Patient Safety and Nursing Practice, n.d.).
In the field of nursing, big data encompasses the analysis of electronic health records, monitoring vital signs and patient data, predictive analytics for patient outcomes, and conducting research using large datasets to uncover trends and patterns in healthcare. For instance, big data can be utilized to analyze patient records and detect early signs of sepsis, thereby preventing its onset. It can also be used to identify correlations between specific treatments and patient outcomes by utilizing large datasets (Thew, 2016).
An example of big data utilization in a clinical setting is during the triage process, where patients can describe their symptoms and receive care recommendations. This technology empowers experienced nurses, enabling them to work more efficiently and provide optimal care for the patient. It allows for appropriate advice and educational materials to be provided to the triage nurse and the patient. I have used this new triage tool for over six months and gained insights into specific words that prompt the most suitable care recommendations. Despite insufficient training from healthcare staff, I have utilized this tool effectively. However, some experienced nurses have not fully embraced it and still rely on their own knowledge and critical thinking when making care suggestions. It is important to note that using a big data tool for triage may hinder one’s ability to engage in critical thinking. It is crucial to remember that despite the incorporation of informatics in nursing, there are still potential risks for errors.
The care recommendations provided are generally applicable. However, I have encountered recommendations that advise the use of ibuprofen and Tylenol for managing pain. The issue with this is that if a nurse lacks critical thinking skills or is new to the field or using the triage tool, they may fail to assess the patient’s health issues and medication list thoroughly. For a patient with hypertension who is using “blood thinners” like Eliquis or Coumadin, the care recommendations would need to be adjusted to ensure they are appropriate for the patient, for example, by avoiding the specific use of Ibuprofen. Ultimately, the nurse’s critical thinking is crucial in ensuring that the care advice provided to triaged patients is suitable.
It’s important to note that it is up to the nurse to provide informative keywords to capture high-risk patients who may need to be seen sooner rather than later and may require an ER visit. Neglecting to provide the symptoms as relayed by the patient, review of health issues, and failure to review the medication list can result in errors in care advice, which in the long run can cause harm to the patient.
Glassman, K. S. (2017). Using data in nursing practice. American Nurse Today, 12(11), Retrieved June 24, 2024, from https://www.americannursetoday.com/wp-content/uploads/2017/11/ant11-Data-1030.pdf
The promise of big data: Improving patient safety and nursing practice. (n.d.). Www.nursingcenter.com. Retrieved June 24, 2024, from https://www.nursingcenter.com/journalarticle?Article_ID=3454042
Thew, J. (2016). Big Data Means Big Potential, Challenges for Nurse Execs. Healthleadersmedia.com. https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs
Tenny Minasyan
Jun 24, 2024 at 3:02 PM
Main Post
One potential benefit of using big data in clinical systems is enhanced personalized medicine. Big data analytics can analyze vast amounts of patient data, including genetic information, medical history, treatment outcomes, and lifestyle factors, to identify patterns and correlations that can guide personalized treatment plans. For instance, researchers have used big data to predict individual patient responses to specific treatments based on genetic markers and historical treatment data (Chen & Asch, 2017). This approach allows healthcare providers to tailor interventions more precisely, improving treatment effectiveness and patient outcomes.
However, a significant challenge of integrating big data into clinical systems is ensuring data privacy and security. Patient data in healthcare is susceptible and must be protected from unauthorized access, breaches, and misuse. With big data systems consolidating large volumes of data from diverse sources, the risk of data breaches increases, potentially compromising patient confidentiality and trust in healthcare providers like HIPPA (Kaelber et al., 2016). One effective strategy to mitigate these risks is implementing data encryption and techniques. For example, encryption algorithms like Advanced Encryption Standard (AES) can protect data both at rest and in use, ensuring that even if data is intercepted, it remains unintelligible to unauthorized parties (Kocabas & Yavuzalp, 2020). Additionally, techniques can remove or puzzle personally identifiable information from datasets used for research or analysis, reducing the risk of re-identification of individuals.
In conclusion, while big data offers significant potential benefits in enhancing personalized medicine through data-driven insights, ensuring data privacy and security remains a critical challenge. By implementing advanced encryption and anonymization techniques, healthcare organizations can effectively mitigate these risks, maintaining patient trust and confidentiality in the era of data-driven healthcare delivery.
References
Chen, J. H., & Asch, S. M. (2017). Machine learning and prediction in medicine — Beyond the peak of inflated expectations. The New England Journal of Medicine, 376(26), 2507-2509. https://doi.org/10.1056/NEJMp1702071
Kaelber, D. C., Bates, D. W., & Jha, A. K. (2016). Health information exchange and patient safety. Journal of Biomedical Informatics, 64, 1-3. https://doi.org/10.1016/j.jbi.2016.09.002
Kocabas, O. F., & Yavuzalp, N. (2020). The role of blockchain technology in health care data security. Health Information Management Journal, 49(1-2), 48-56. https://doi.org/10.1177/1833358319859605
