NURS 6051 Walden University Nursing Big Data Technology

Big data comprises of huge sets of discrete facts collected, that can be stored and analyzed stored with statistics models and computer systems, to arm the healthcare personnel like a nurse with predictive and prescriptive actions to positively impact the quality of a patient’s health(McGonigle & Mastrian, 2017). This data can be structured (Electronic Health Records) or unstructured data like the MRI images. When done right, there are multiple advantages of using big data including; improving quality of clinical decisions, shorten diagnostic times, quick access to data for analysis, explore new research methods, and cost cutting(Wang, Kung, & Byrd, 2018).

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One area where I have seen the impact of big data in my practice is in the care of patients with diabetes that currently make up about 9% of the US populations. The use of glucose check device like the Nova Statstrip connected with computer system makes the results available to all practitioners when it is docked reduces errors of giving wrong results verbally which can tremendously impact patient’s care and treatment(Rabiee et al., 2010). For example, the collected data is connected to the patients electronic health records and instantly available to nurses like myself at the point of care. This provides avenues to detect trends and patterns of hyperglycemia and hypoglycemia episodes in the patient rapidly. This helps to facilitate both acute and long term therapy adjustments(Roessner, 2019). There are systems that connect this glucose monitoring with patients smart phone devices. This gets the patient involved in their care as well as track and monitor their diet and glucose levels. This is therefore one area that I have experienced the value of big data both in helping me collect data, process it into information. The information is then combined with the patients history in the in the HER to provide me with knowledge to make wise decision on the medication type, type of life style changes and education to provide the patient.

One area where I have faced challenges with big data challenge has sometimes been on the volume, velocity and meaning of some of the unstructured data (MRI, CTs, ) that is generated. Sometimes the terminologies that are used or statistical terms become meaningless and build some resistance in using the results of big data analysis. When you begin to mention names and acronyms like HADOOP, SEMMA, CRISP-DM that are used to pull critical information from the treasure trough of data from genomic research, registries, EHRs and devices then am lost. One particular example that I have observed is the difficulty of comprehending and reading some of the visual outputs of unstructured data

Taking full advantage of the benefits from big data in health care requires an extensive approach in my opinion to build the necessary data competencies involved to facilitate communication. It is clear that many bed side nurses can collect data, process and use it to deliver care. However apply a lot of data mining tools to correctly use the trends and patterns detected is a challenge that I think has to be bridge from educational programs at the undergraduate level. I now understand why some new graduates that I train on the floor are taking classes in digital media, programing and computing. This can at least bridge the communication gaps between tech savvy information technologist and clinical savvy nurses. The second area I think will help it instituting a data competency culture at the unit. This can be achieved through simple workshops that expose the ways big data is been used on the nursing floor, how it is impacting decision making and area where folks can learn how to use.

McGonigle, D., & Mastrian, K. (2017). Nursing Informatics and the foundation of knowledge. Burlington, MA: Jones & Bartlett Publishers.

Rabiee, A., Magruder, J. T., Grant, C., Salas-Carrillo, R., Gillette, A., DuBois, J., . . . Elahi, D. (2010). Accuracy and reliability of the Nova StatStrip® glucose meter for real-time blood glucose determinations during glucose clamp studies. Journal of diabetes science and technology, 4(5), 1195-1201. doi:10.1177/193229681000400519

Roessner, K. (2019). Big Data Are Changing Diabetes Management in Big Ways. Retrieved from https://www.abbott.com/corpnewsroom/diabetes-care/big-data-are-changing-diabetes-management-in-big-ways.html

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, 3-13. doi:https://doi.org/10.1016/j.techfore.2015.12.019