As dwellers of the 21st century, we are generally largely unaware of something that we are endlessly surrounded by. A “something” so pervasive that it’s effectively similar to the air we breathe. Just as we go about our lives immersed in a gaseous sea of nitrogen and oxygen, we are also continually surrounded by digitally generated data.
It’s not an exaggeration to assert that modern life will grind to a halt if that supply of data stuttered to a halt, rather as it would if the air we take for granted thinned and depleted. Digital data is everywhere and, unsurprisingly, experts in all fields are utilizing it to make informed decisions and to plan ahead.
When a surgical nurse specialist explains to a patient the risks of complications during eye surgery, he or she is drawing on the data from studies and previous procedures that have been compiled and analyzed to inform that guidance. In the internet age, and especially as the era of the ‘Internet of Things’ gathers momentum, information takes the form of digital data, not least because so much commerce, research, publishing and personal communication occurs online and through social media platforms.
The sheer volume of this data has reached a mind-bogglingly stupendous scale. Global leader in market and consumer data research Statista, for example, has calculated that the total amount of data created, captured, copied and consumed globally will total 180 zettabytes by 2025, where a single zettabyte amounts to one billion terabytes.
This is clearly beyond the reach of human cognitive powers of computation. Even so, humans somehow must understand and make use of this data in order to craft viable plans and forecasts, and this applies to leading decision-makers in healthcare as well as meteorology, marketing and supply chain management.
In this article, we’ll focus on one aspect of the first of these fields: decision-makers in the field of nursing in the context of healthcare systems. These are the people who must analyze immense quantities of health data in order to extract practical insights from it.
What is health data?
Health data, essentially, refers to any data concerned with human health, whether in the form of data generated by individual patients during healthcare or in the form of health-related data derived from large, complex populations. The information arising from these sources is gathered by a range of health information systems (HIS) and a battery of related technological instruments, and it feeds into the day-to-day work of healthcare professionals such as doctors and nurses as well as insurance firms and government agencies.
A patient walking into a clinic or hospital isn’t simply an individual; they are also embedded in specific demographic trends related to family health history, location, socioeconomic status, race and physical and temperamental disposition. Modern healthcare systems rely upon data related to these multiple dimensions of a person’s clinical presentation, which is collected, stored, shared and analyzed courtesy of a variety of technological tools.
- Electronic health records (EHRs)
- Personal health records (PHRs)
- Electronic prescription services (E-prescribing)
- Patient portals
- Master patient indexes (MPI)
- Health-related smartphone apps
One inevitable effect of all this data collection, of course, is that the data mountain in need of analysis – the vast repository we refer to as ‘Big Data’ – grows larger and ever more complex with every passing second.
Let’s move on at this point to an exploration of the importance of data analysis in nursing.
The role of data analysis in nursing
While practicing nurses in hospitals, clinics and physicians’ offices have a duty to be aware of the clinical data related to their work, at the level of healthcare systems more generically, a broader scope is needed. Nurses are playing a crucial role here too, thanks to the rise of executive leadership roles in the profession, most notably the Chief Nursing Officer (CNO).
Nurses who occupy these high-level roles have built on years of clinical experience as registered nurses (RNs) to progress through advanced academic and clinical training to take on top-tier leadership positions in hospitals or state-wide healthcare systems. Most have gained doctoral qualifications on top of their entry-level and subsequent credentials, such as a Bachelor of Science in Nursing (BSN) and Master of Science in Nursing (MSN).
Studying for a Doctor of Nursing Practice (DNP) degree while practicing full-time as a nurse is a tough business, but today many with the ambition, ability and resolve to reach these prestigious CNO leadership roles have a new option. Growing numbers of well-established brick-and-mortar universities such as Texas’s Baylor University, are starting to offer online DNP leadership programs such as the Doctor of Nursing – Executive Nurse Leadership (DNP-ENP) which are uniquely tailored to the demands of this crucial executive role.
CNOs make decisions affecting the delivery and operations of healthcare, and in order to do so, they must possess the ability to craft rigorously data-driven business strategies and transformative care models. Without these data analysis capabilities, their work would be akin to ‘shooting in the dark’.
For example, with the growing demand for patent-centric medical care, the need for reliable predictive and preventive measures has intensified. The ability to analyze and draw from both current and historical data – predictive analytics – is critical to the success of these initiatives. Models for predicting future outcomes are informed by advanced data mining and machine learning technologies to identify patterns and trends. Healthcare professionals can benefit their patients with this predictive knowledge as it alerts them to potential health risks ahead based on analyses of patient behavior and medical history.
When machine learning technologies are harnessed in Big Data analysis, CNOs, other health executives and clinical practitioners alike can be informed by robust, data-rooted guidance on the optimal course of healthcare action for the best outcomes for individual patients. This is known as prescriptive analytics.
Data analytics in nursing and healthcare is here to stay
A CNO’s familiarity with data acquisition, data management, data analysis and data interpretation can assist them in crafting the wisest and most optimal strategies for improved patient outcomes, from providing practicing nurses with the best evidence-based treatment guidelines to ensuring appropriate nurse staffing levels in wards and clinics.
The age of Big Data affects us all, but the nursing profession is harnessing its power to generate improved healthcare outcomes for all demographics.