In the last 10 years, a wholesale transformation of the health care industry has taken shape as systems, providers, and payers tapped into the wealth of knowledge contained in "big data." Nursing education is missing from this critical new area of discovery and innovation - we are absolutely nowhere to be seen! A term introduced in the 1990s, big data refers to groups of information too large to be analyzed and manipulated with existing software. Big data includes structured as well as nonstructured data, such as the free-form information contained in chart notes or nursing notes. In 2016, the National Institutes of Health defined big data as information assets characterized by high volume, velocity, and variety that require specific technology and analytic methods for its transformation into use (Mallappallil et al., 2020).
As nursing's educational professionals, we must acknowledge the invaluable impact of big data on pedagogy, especially its ability to strengthen nursing education. This editorial explores three significant results that come from integrating big data into nursing education: 1) a revolutionary recasting of nursing education to focus on real-world skillsets and critical thinking, 2) a sharing of data now contained in "information silos" to better align curricula with licensure, and 3) a massive pivot to focus educational research on never-before-available large data sets.
INTEGRATING BIG DATA INTO PEDAGOGY
Big data offers an immense opportunity to revolutionize pedagogy with large-scale, trended information that has never before been available for study. Rather than be stymied by the insulation and isolation inherent in internal data, nurse educators can compare their efforts to those of their peers nationwide - down to class, student demographic, and accommodation, if any. However, I continually wonder why nursing is not taking advantage of this wealth of information. Which nurse educator will conduct the research needed to determine the Nurse Education Minimum Data Set agreed for a data dictionary? We need such information now to make informed, evidence-based decisions on many levels.
Big data and its powerful toolsets aggregate vast amounts of information from disparate sources to reveal trends and produce data-driven best practices to improve nurse readiness for practice. It is critical that nurse informaticians and researchers analyze big data, including deidentified encounter data and aggregated information from across the continuum of care. This analysis will pinpoint the information needed for a revamped nursing curricula that enables students to develop the critical thinking skills essential for a digital health care delivery environment. Taking a data-driven approach to pedagogy empowers educators to tailor their teaching methods to address students' specific learning needs, making nursing education more personalized and more effective.
We can learn a lot from our partners in clinical practice. Take, for example, the focus of the doctor of nursing practice (DNP) degree and its curricula, now centered on the complexity of health care systems, operations, and technologies. Graduates learn to navigate and manipulate this complexity in favor of patient outcomes and systems efficiency, a much different emphasis than the original vision of the DNP that emphasized clinical knowledge (American Assocation of Colleges of Nursing, 2023). With 75 percent of all hospital beds affiliated with complex, multifacility health systems (American Hospital Association, 2023), the ability to understand, navigate, and coordinate highly complex systems and operations is critical. Similar systems content focused on teaching and learning in higher education is not included in most PhD or EdD curricula. Our educators are not being prepared to practice in the system of higher education, nor are they being prepared to teach. Big data could provide nurse educators insights into best teaching practices as well as best practices in recruitment and retention of students.
Each year, we collectively graduate 250,000 nurses, each of whom sits for the same licensure exam in all 50 states and provinces. Of those who receive licensure, 56 percent leave the profession in the first year, and 50 percent of the remaining nurses quit within the first five years (Nurse Money Talk, n.d.). We need to reexamine our admission criteria and ask, "Can more stringent preacademic admission screening stem this alarmingly high rate of loss? What will collective big data teach us if we mine it?" No one knows, even though the data are there waiting.
ELIMINATING UNWARRANTED PROPRIETARY STATUS
Academic institutions and professional organizations envelope their data in an all-inclusive and, in my opinion, unwarranted proprietary status. Is it any wonder that nursing research takes 17 years to reach the bedside? Although controlling and limiting access to closely held data may be the norm today, this practice cannot continue. Democratization of nurse data is on the rise. Faculty members need to persistently encourage academic leadership to distinguish three very different types of data: 1) information that needs protection because of patient and student confidentiality and regulatory oversight; 2) data that, once integrated into pedagogy, encourages innovative learning; and 3) information that, through big data, could be shared to fuel collaboration in patient care and teaching/learning innovations and research. I believe that only a small portion of today's protected data actually belongs in that first category.
The pooling of data is required if we are to mount a harmonious national collaboration to drive change across nursing higher education and the health care landscape. Using sophisticated big data toolsets, educators can develop standard analytics to gauge how variability in schools' curricula impacts licensure results. For example, nursing schools working in a collaborative data-sharing model could use pooled best practice data to expand pedagogy and build student skills that far exceed the minimum baseline of licensure and better protect the health care consumer.
National boards of nursing and professional organizations, the holders of this much needed data, need to take action now to begin sharing critically important information - before outsiders answer best practices licensure and regulatory questions for us. Carpetbaggers are coming for our data, and I can guarantee they will make money from it. Hospital revenues and profit must not be made on the backs of nurses. There is danger that with a capitalist approach to the market, well-educated nurses will be marginalized into "generic health care workers" on the lower rungs of the pay scale. We have already seen, for example, how traveling nurses receive substantially higher compensation and more scheduling flexibility than in-house staff nurses.
Advocating for shared data brings with it a responsibility to anonymize information and protect it from increasingly aggressive digital threats. Ethical guidelines, which are constantly shifting and refined within an ever-changing global threat environment, offer proven ways to harness the power of big data responsibly while ensuring confidentiality and regulatory compliance. Educators and researchers have a professional responsibility to monitor and implement data safeguards actively and consistently, keeping emerging and dominant types of threats from nefarious parties.
MINING BIG DATA OPPORTUNITIES
When academia stops seeing its data as strictly for their internal educational use, that vital information can be added to the vast pool of data relevant to health care operations, patient care, and nursing practice. To advance the science of nursing and nursing education, only big data can deliver the large volumes of data required to validate discoveries, insights, and conclusions. It is time for nurse educators and academic leaders to leverage this body of cross-institutional knowledge, enriched by academia's collective wisdom, to drive nursing research and improve student outcomes, patient care, and nursing practice.
Big data analysis and interpretation requires data mining expertise, including technical skillsets encompassing statistics, programmatic modeling, and data engineering. Data mining specialists also need two important soft skills: strong communication capabilities and creative problem-solving abilities. Nurses with these skillsets make ideal candidates for data-mining roles in multifacility health care systems, higher education, and nursing research. These skillsets need to be included in our doctoral curricula in order for nurse researchers to be leaders of collaborative teams making an impact in education and clinical practice.
CONCLUSION
Our profession stands at a precipice - secure our future by integrating big data into pedagogy or be defined by others as marginalized, generic health care workers on the lowest rung. Outside nursing education, other educational institutions, industries, and corporate entities are already capitalizing on big data by mining its hidden insights. Even our "customers," multi-health care organizations across the country, are pioneering the use of machine learning (ML), artificial intelligence (AI), and advanced analytic toolsets to amass large data sets from their facilities. For example, data mining is beginning to reveal the analytics that enable a deeper understanding of how to screen candidates for nursing employment. As nurse educators, we must do the same to protect our professional autonomy and ensure that nursing remains at the forefront of clinical innovation. By sharing data responsibly, we can verify the most effective and cost-efficient ways to treat health care conditions based on data, not intuition, and change pedagogy to align with that knowledge. We need to reexamine pedagogy, learning platforms, and knowledge acquisition now and earn the credibility and influence that come from this new age of data-driven nursing. Equally important, our students and patients deserve the optimal outcomes that come from leveraging all the knowledge that exists.
I believe the most significant barrier to our use of big data is nursing's lack of knowledge. Based on my own recent conference attendance, learning about big data ranks at the bottom of nurses' priorities. Although hundreds of nurses and nurse leaders pack ballrooms to gain knowledge about improved communication strategies, next door, in a small room, I have seen only a handful engaged in a big data session. Professional organizations need to position big data as a top-of-the-conference keynote across their educational offerings. Every nurse, not just nurse informaticians, must value and understand why nursing data are critical to advanced initiatives such as AI and ML.
What data are we missing to ensure that AI and ML reflect nursing's unique view of best practices in education and patient care? Without critically important nursing data, AI and ML will create algorithms that override nursing knowledge. Big data manipulation and analysis may not be a nurse educator's core competency, but every nurse needs to know how to pose questions, comprehend the democratization of nursing data, and comprehend the swiftly changing ethical issues that impact how data inform the algorithms used in AI and ML. ML takes place with only the data it has. If we don't get our data bases in there to mine, we are poised once again to be perceived as generic health care workers. When invisible, we are out of sight. We must be in clear view of all to survive and thrive!
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