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Big Data Risks and Rewards

Updated: Apr 1, 2022

by Mozart M. Telles, RN, BSN

March 30, 2022

Benefits of using Big Data

A potential benefit of using big data is the possibility of improving precision in nursing tasks while improving patient outcomes since companies reduce the number of nurses and increase caseload. A new branch of nursing called precision health employs big data sets that merge omics (i.e., genomic sequence, protein, metabolite, and microbiome information) with medical information to improve diagnosis, treatment, and inhibition of specific diseases for each patient (Fu et al., 2020). Also, treatments can be more reliable because when the sample size increases, certainty and measured correlation can be substantially higher (Niven & Deutsch, 2012). Wang et al. (2018) state that through comprehensive or well-detailed patient data, health care providers find it easier to predict the response to specific treatments. Furthermore, big data can generate new insights into risk factors (Mcgonigle & Mastrian, 2022, pp. 537–558).

A potential challenge of using big data

Jennifer Thew, RN (2016) discussed the meaning of big data and the challenges that big data brings to nurse executives. In her analysis, she states that the Chief Nursing Executives (CNEs) are drowning in information that cannot be effectively processed to find relevant data; therefore, CNEs opt for a “person-on-person debate” rather than discussing a “persuasive business case based on data.” Too much information without meaning is a challenge that big data suffers.

In “Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations” by Wang et al. (2018), the authors “identify five big data analytics capabilities from 26 big data cases, present several strategies for being successful with big data analytics in healthcare settings, and provide a comprehensive understanding of the potential benefits of big data analytics.” Demonstrating that the problem is not the amount of data, as Jennifer Thew (Thew, 2016) discusses, but instead the ability of the nurse managers to process that information efficiently. Leading to a potential challenge to big data, increasing the risk of data being forgotten (lost) and not reviewed (Thew, 2016).

A proposed strategy that mitigates the challenge

We know that there is a tremendous amount of information to sift through, but it can be interpreted by using algorithms and the computational power that we have today to make sense of data. Mcgonigle and Mastrian (2022, pp. 541-542) say that “Big Data” can be explored and refined to show the desired outcome. They cite Moskowitz et al. (2015), who states that the issue is that nurses are not prepared enough to deal with big data, and “medical education at all levels must come to address data management and utilization issues as we enter the era of big data in the clinical domain.”

“The informatics competency helps nurses use information and technology to communicate, manage knowledge, mitigate error, and support decision-making at the point of care” (Glassman, 2017).

The best strategy is for a CNE to work with nurse informaticists, statisticians, and IT to collect the raw data and produce a well-developed algorithm to analyze this data and produce results.

Wang et al. (2018) say that the first step is to “explore the best practice of big data analytics architecture in healthcare.” Mcgonigle and Mastrian (2022, pp. 537–558) say that data management is essential to accomplishing the desired outcome with any algorithm, and AIoT may come to the rescue.

On that account, machine learning will help reduce the fatigue that big data brings. With concise programing, artificial intelligence will separate the information required from the noise. Deep learning can explore deeper relations between the data collected, showing better results than linear processing, and demonstrating that AI can be more precise in specific tasks (Mcgonigle & Mastrian, 2022, p. 557).


Fu, M. R., Kurnat-Thoma, E., Starkweather, A., Henderson, W. A., Cashion, A. K., Williams, J. K., Katapodi, M. C., Reuter-Rice, K., Hickey, K. T., Barcelona de Mendoza, V., Calzone, K., Conley, Y. P., Anderson, C. M., Lyon, D. E., Weaver, M. T., Shiao, P. K., Constantino, R. E., Wung, S.-F., Hammer, M. J., & Voss, J. G. (2020). Precision health: A nursing perspective. International Journal of Nursing Sciences, 7(1), 5–12.

Glassman, K. (2017, November). Using data in nursing practice.

Mcgonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed., pp. 537–558). Jones & Bartlett Learning.

Moskowitz, A., McSparron, J., Stone, D. J., & Celi, L. A. (2015). Preparing a New Generation of Clinicians for the Era of Big Data. Harvard Medical Student Review, 2(1), 24–27.

Niven, E. B., & Deutsch, C. V. (2012). Calculating a robust correlation coefficient and quantifying its uncertainty. Computers & Geosciences, 40, 1–9.

Thew, J. (2016, April 19). Big Data Means Big Potential, Challenges for Nurse Execs | HealthLeaders Media.

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.

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