At the American Nursing Informatics Association 2019 conference, Dr. Roy Simpson, in his Keynote Address “Kaleidoscope: Twists and Turns in Big Data” put forth a challenge to nursing to “put the ‘N’ in STEM” (Simpson, 2019). In his presentation, he charged nurses to become more familiar and comfortable with big data analytics to scientifically use technology in our work with statistics-based data manipulation for improved safety, quality, and outcomes. Further, he posited that to enhance nursing education and better research, and to prove the value of nursing, we need to begin to strategically use our data to discover hidden patterns and trends to measure clinical and administrative performance. His account of the steps required to move forward with using big data, and nurses needing to learn how to use advanced programming techniques to aggregate and analyze it is challenging, yet ultimately necessary for the profession. Thus, the need for nurse data scientists. As the movement to advance people of all ages and industry experience into science, technology, engineering, and math (STEM) continues, for nursing, there is not a consensus in the U.S. if the profession falls into the STEM disciplines. Some agree that it does, while others acknowledge nursing as a domain in STEM (Hedgecock, 2016). There has also been a movement to add an M for medicine (STEMM) to introduce more healthcare professions into the disciplines that further complex technology-based problem solving and innovation to tackle societal challenges and transform communities, (Caple, 2017).
Today’s critical healthcare technological trends that include improving health in rural communities, incorporating social determinants of health into practice, and realizing whole person care with precision health (Vogenberg & Santilli, 2018), can all benefit from the talents and skills of nurse informaticists as data scientists decisively placing nursing into the STEM discipline. For instance, by programming code for predictive analytics, nurse informaticists can help clinicians move siloed data to the point of decision since all expert decision makers need clear, timely feedback to develop, maintain and apply clinical expertise. One way to improve this is to apply data science to shorten the feedback cycle and eliminate current retrospective reporting mechanisms (Schmidt, Donatelli & Meyers, 2014).
Nursing skills are equivalent to the skills needed by healthcare data scientists. All nurses have to manage day-to-day patient care and ensure tasks get done in the right time frame; this requires substantial documentation and data capture with an understanding of what the data means which can transform a regular nurse role to an informatics nurse role and beyond (Holman, n. d.). Nurse informaticists already have inherent creativity and practice necessary data scientist non-technical skills of intellectual curiosity, and effective communication, business acumen, and a strong sense of teamwork (Quora, 2017). Along with these strengths, nurses are well-positioned to move into data science roles, having the innate skills of critical thinking, problem-solving, and with the strong abilities to evaluate risks and make rapid process improvements (Gutierrez, 2018).
In preparation to upskill or hire tech-savvy workers, such as nurse informaticists for data science, healthcare organizations should look at how to get the most out of their existing staff by enhancing their skills, as competition for talent in the technology space is fierce, and a robust and secure data infrastructure is essential (Schreiber Radis, 2018). This is where nurse data scientists can be leading contributors. With the transformation of nursing roles due to novel technologies, now is a perfect time for nurse informaticists to move into data science. There is a deep need to bridge the gap between nurses at the front lines and in non-clinical settings, such as quality improvement and risk management and nurse scientists trained at the Doctoral level to advance clinical data analytics in healthcare organizations. Teaching nurse informaticists advanced programming techniques at all levels of education and in various settings can help fill this void.
Dr. Simpson’s final point in his presentation was for nurse informaticists to get started and lead the charge by example as we embark on a critical mission to learn advanced data analysis, become authorities as nurse data scientists and transform our role from data creators to expert data users to improve the profession (Simpson, 2019). Led by the nursing informatics specialty, nurses need to become genuine players in the STEM game and become forerunners in mastering healthcare data analytics.
Citation: Carroll, W. (Summer, 2019). Putting the ‘N’ in STEM: A call for nurse data scientists. Online Journal of Nursing Informatics (OJNI), 23(2). Available at http://www.himss.org/ojni
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Schreiber Radis, E. (2018, December 26). Top jobs that pave the way for becoming a data scientist. Retrieved from https://opendatascience.com/top-jobs-that-pave-the-way-for-becoming-a-data-scientist/
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