Big data is the vast collection of multiple data outlets combined into a singular, massive storage space of data (Thew, 2016). As part of a clinical system, big data is beneficial for many reasons, but the largest—and most important reason—may simply be the benefit of improving patient care. Specifically, big data can improve patient care through pattern recognition after thorough analysis of all data collected (Wang, Kung, & Byrd, 2018). The reason that this would be beneficial to improve patient care is because if patterns are recognized based off data collection, this can then aid in targeting the necessary changes needed to be made to medications, treatment, or other decisions of care. An example would be with cancer patients because, being that cancer is a disease specific to the cellular level within patients, the attainment of multiple data outlets and synthesis of it all could result in a uniquely developed treatment plan for each patient (Pastorino et al., 2019).

However, an issue with big data in general is that it is typically difficult to track everything accurately and effectively synthesize it all into the most necessary information. It’s very difficult and time consuming to efficiently analyze vast quantities of data, especially if you are trying to access them all from multiple outlets (Pastorino et al., 2019). One strategy I have observed is through the utilization of all the technology within hospitals and clinics these days. The use of technology greatly diminishes the challenge of trying to accurately gather big data because it simplifies the acquisition and storage of data electronically. An example would be with electronic, portable devices. Nurses often have smart phones given to them by their employer to help track and store the data of their patients (Glassman, 2017). This smartphone technology can often be linked to other electronic devices and maintains the nurses more easily connected to their patients’ progress.

Another example is the usage of technology worn by patients—such as monitors for diabetics. These are often connected to smartphone technology to monitor blood glucose levels remotely. The collection of all this data—from the constant fluctuations of glucose levels to the carbohydrate and insulin intakes, to everything else in between—is all able to easily be tracked and accurately documented via the same shared electronic devices. This easily facilitates the process and makes data collection, analyzation, and pattern recognition all much more achievable. Thus, the ultimate goal of improved patient care is more attainable because you are being able to create more accurate and quality results from the data collected.

References

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

Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., & Boccia, S. (2019). Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. European journal of public health, 29(Supplement_3), 23–27. https://doi.org/10.1093/eurpub/ckz168

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.