What Every Nurse Should Know About Big Data

Like just about every other industry, healthcare is embracing the concept of “big data.” The term has been defined in many ways, but in general it refers to a large data set that has been collected through multiple computers and then analyzed in such a way that associations, trends, and patterns of human behavior are revealed. When you shop on Amazon and receive customized product recommendations that reflect not only your tastes but your budget, that’s an example of a company leveraging big data to drive business and improve customer relationships.

In healthcare, big data has the potential to streamline operations as well as offer insights about preventing, diagnosing, treating, and evaluating diseases and outcomes—both at the patient and the population level. The EMR is the major source of medical and treatment data, but there is currently an industry-wide discussion about incorporating other data as well, including patient-generated information that currently resides in mobile health and fitness apps.

When nurses are involved in documenting patient data, they can include relevant information about the patient’s home environment, support system, behavioral factors, lifestyle choices, health literacy, and more. This data comes from the types of observations that nurses are trained to make, and including it in big data sets can lead to breakthroughs for individual patients as well as communities and populations.

There is one major barrier to using big data in healthcare: the need for data that is standardized to the point it is comparable. If your employer has begun pushing for standardized terminology in your nursing documentation, this may be why. When the same concept is charted in different ways, it becomes difficult to identify patient or treatment trends over time. So if you’re charting “shortness of breath” while another nurse is shortening it to “SoB” and yet another nurse is documenting “dyspnea,” it becomes more difficult for IT systems and analysts to pull data for use in research projects or quality-improvement projects. Nurses can further the goals of big data by adhering to documentation guidelines that are set forth by their organizations. This not only helps your facility, but it can help you as well—for example, by automatically calculating patient acuity so staffing needs can be determined based on actual workloads and nursing skill sets. Adequate staffing definitely benefits the patient, but it also benefits nurses by reducing stress and burnout.

If you’re accustomed to typing observational notes into the EMR, you should be aware that this type of “free text” is considered unstructured data—which is harder to analyze and compare than structured data. This is why many electronic charting systems rely on checkboxes and pulldown menus to restrict the way data is entered into the system. It’s also good reason to use research-based assessment scales—like the Braden scale for pressure ulcers, which has been successfully used to develop evidence-based prevention protocols via big data. There are also efforts underway to mine data from the EMR that can help clinicians detect patterns that are indicative of early sepsis.

Specialized, consistent documentation is an important way nurses can remain visible and have their contributions recognized in the digital age.

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