
All roads lead to data collection when discussing the enhancement of the nursing workforce and the workforce’s impact on healthcare systems. Purposeful nursing workforce data collection can assist with answering questions and generating innovative ideas on how to build an improved nursing workforce. This post will briefly share a DNP/Ph.D. perspective on enhancing data collection and improved information infrastructure for effective workforce planning reflected in the National Academy of Medicine’s Key Message #4: Effective workforce planning and policymaking require better data collection and an improved information infrastructure for effective workforce planning (NAM, 2010). We will also introduce the 2024-2028 NAM goals as the healthcare paradigm evolves.
Workforce Data Collection
Werley & Lang (1988) defined the Nursing Minimum Data Set (NMDS) as ‘a minimum set of elements of information with uniform definitions and categories concerning the specific dimensions of nursing, which meets the information needs of multiple data users in the health care system’. Nursing, in particular, has found success with MDS implementation, as thirty state nursing workforce centers currently use an MDS to collect data (Freguia, 2022).
In Texas, data and research about the nursing workforce are captured through the Texas Center for Nursing Workforce Studies (TCNWS), a part of the Center for Health Statistics at the Department of State Health Services.TCNWS produces publications on the trends and demographics of the nursing workforce in Texas. Texas Board of Nursing’s Licensure Renewal data collects nursing workforce demographics. Summary reports and infographics on the state and regional level may include data from multiple sources, such as education and employer surveys.
TCNWS worked with Global Data (formerly IHS Markit) to use the Health Workforce Simulation Model (HWSM) and the Healthcare Demand Microsimulation Model (HDMM) to develop supply and demand estimations for nurses in Texas. HWSM reviews the prospective career choices of individual nurses in Texas to predict what supply might look like annually through 2030. The HDMM predicts demand for healthcare services and providers based on population demographics, healthcare service usage, and staffing trends. TCNWS collaborates with the Texas Board of Nursing to administer its annual survey of nursing education programs in Texas. Every October, the Nursing Education Program Information Survey (NEPIS) is distributed to all vocational, professional, and graduate-level nursing education programs.
Additional resources, such as data collection related to retention rates within clinical environments, can be of value in identifying issues early on and addressing them at the practice level. Exit interviews assist in identifying gaps in not only recruitment efforts but also retention efforts across the nursing workforce. In a random sample of 700 registered nurses from a Board of Nursing’s list of currently licensed registered nurses (n = 2300). Nurses completed and returned four survey tools to the research team. This survey (36.38%) found that a sense of belonging, and value can contribute to the retention of data through validated surveys to front-line staff (Reinhardt et al., 2020; Beck et al., 2018). This data can be used to employ interventions to enhance nurse satisfaction within all practice settings.
Information Infrastructure
Coordination of data collection between various nursing organizations and governing bodies via a refined information infrastructure is necessary for improving the nursing workforce. An infrastructure is a system where several groups conduct activities that, if disrupted, may severely impact a country or countries and cause a significant socio-economic crisis (Dixon et al., 2023). Information infrastructure was defined by the Information Infrastructure Task Force 2 as “… a seamless web of communications networks, computers, databases, and consumer electronics that will put vast amounts of information at users’ fingertips” (“The National Information Infrastructure,” 1994.; “US Department of Health and Human Services,” 2013). Information infrastructure is the context within which information and communications are managed (Feather et al., 2003). Health information infrastructure unifies the health data collected and maintained by many different organizations that may use unique and incompatible terminology (Dixon et al., 2023; Herrera et al., 2019).
Local, state, and national consortiums and collaborative conferences can assist in sharing resources to build infrastructure. For example, online learning communities through the National League for Nursing, the Association for Nursing Professional Development, and the American Association of Colleges of Nursing have numerous resources to collaborate across academia and practice settings to share best practices and assist in building solid infrastructure. Examples include learning communities, discussion boards, and online webinars. This can also be found in peer-reviewed articles and case studies.
Workforce Planning
Inconsistency with forecasting
Squires et al. (2017) evaluated forecasting models and their contribution to workforce planning policies for nursing professionals. It also highlighted the strengths and weaknesses of existing approaches. Thirty-six studies were reviewed, the majority of which came from the USA. Results from this evaluation noted that forecasting methods were biased towards service utilization analyses and inconsistent across studies. At that time, methods for nurse workforce forecasting were inconsistent and did not account for socio-economic and political factors that can influence workforce projections. Precise nursing workforce forecasting can assist nurse managers, administrators, and policymakers in being cognizant of the supply and demand of the workforce (Shern et al., 2016). This will, in return, produce and preserve a competent current and future workforce.
In a systematic review of forty-eight articles related to the factors affecting the nursing workforce shortage in hospitals, the main barriers were policy and planning, training and enrollment, and nursing staff turnover (Tamata et al., 2023). Academic retention strategies and innovative programs must be employed to support well-being and ongoing work-life balance.
The pandemic impacted students who have recently entered academia. Challenges of social acclimation, anxiety, mental health, and organization skills may be lacking from the disruption during their younger years during the pandemic. For example, students in high school during the pandemic are now entering the academic environment and continuing online studies, but they need dedicated support groups.
Ongoing National Academy of Medicine Goals
As we continue to support ongoing change in the healthcare landscape, it is essential to note the updated National Academy of Medicine Goals for 2024-2028, including:
Science: Advance science, technology, and innovation as a foundation for health and medicine.
Action: Lead, inspire, and catalyze evidence-informed action on urgent, critical issues and long-term societal challenges to health.
System Transportation: Catalyze transformation toward a health system that is effective, efficient, equitable, affordable, and continuously learning.
Equity: Lead by integrating inclusion in all we do.
Readiness: Ensure that the NAM and the nation are ready to shape the future of health and medicine.
Call to Action
Accurate data collection is crucial for understanding the demands of nursing workforce planning. As a professional nurse, please provide the most accurate information regarding the nursing workforce in surveys. Academic and practice transparency and honesty will help organizational leaders and governing bodies evaluate and implement strategies to improve the nursing workforce. A standardized mechanism for data collection and monitoring the nursing workforce will assist organizational leaders and governing bodies in making evidence-based workforce planning decisions. Results of surveys may come from professional nursing organizations, state agencies, healthcare institutions, and nursing education programs. With all the different data points and a need for better information infrastructure, successful strategies for workforce planning may not reach the expected outcome of nursing leaders (Weller-Newton et al., 2021). We must move forward to innovate and adapt to the changing times to streamline our approach to maintaining the nursing pipeline.
References
Beck, A. J., Singer, P. M., Buche, J., Manderscheid, R. W., & Buerhaus, P. (2018). Improving data for behavioral health workforce planning: development of a minimum data set. American journal of preventive medicine, 54(6), S192-S198. https://doi.org/10.1016/j.amepre.2018.01.035
Dixon, B. E., Broyles, D., Crichton, R., Biondich, P. G., & Grannis, S. J. (2023). Architectures and approaches to manage the evolving health information infrastructure. In Health Information Exchange (pp. 199-215). Academic Press. https://doi.org/10.1016/B978-0-323-90802-3.00001-0
Feather, J., & Sturges, P. (2003). International encyclopedia of information and library science. Routledge.
Freguia F, Danielis M, Moreale R, Palese A. Nursing minimum data sets: Findings from an umbrella review. Health Informatics Journal. 2022;28(2). doi:10.1177/14604582221099826
Herrera, L. C., & Maennel, O. (2019). A comprehensive instrument for identifying critical information infrastructure services. International Journal of Critical Infrastructure Protection, 25, 50-61.https://doi.org/10.1016/j.ijcip.2019.02.001
National Academy of Medicine. (2024).2024, August 25. NAM Strategic Plan 2024–2028. https://nam.edu/wp-content/uploads/2024/01/NAM-Strategic-Plan-2024-2028.pdf
Reinhardt, A. C., León, T. G., & Amatya, A. (2020). Why nurses stay: Analysis of the registered nurse workforce and the relationship to work environments. Applied Nursing Research, 55, 151316. https://doi.org/10.1016/j.apnr.2020.151316
Shern, L., Butler, A. S., & Altman, S. H. (Eds.). (2016). Assessing progress on the Institute of Medicine report The Future of Nursing.
Squires A, Jylhä V, Jun J, Ensio A, Kinnunen J. A scoping review of nursing workforce planning and forecasting research. J Nurs Manag. 2017; 25: 587–596. https://doi.org/10.1111/jonm.12510
Tamata, A. T., & Mohammadnezhad, M. (2023). A systematic review study on the factors affecting shortage of nursing workforce in the hospitals. Nursing Open, 10(3), 1247-1257. https://onlinelibrary.wiley.com/doi/epdf/10.1002/nop2.1434
Weller-Newton, J. M., Phillips, C., Roche, M. A., McGillion, A., Mapes, J., Dufty, T., … & Haines, S. (2021). Datasets to support workforce planning in nursing: A scoping review. Collegian, 28(3), 324-332. https://doi.org/10.1016/j.colegn.2020.09.001
Werley HH, Lang N. Identification of the nursing minimum data set. New York: Springer Publishing, 1988.
US Department of Health and Human Services. (2013). Health Resources and Services administration. National Center for Health Workforce Analysis, 2013-2025.
Sharisse A. Hebert, DNP, APRN, FNP-BC
Assistant Professor
Prairie View A&M College of Nursing DNP Program Coordinator
6436 Fannin St, Houston, TX 77030
Phone: (713) 797-7050
Email: sahebert@pvamu.edu
Amanda Garey, Ph.D., RN, NPDA-BC, EBP-C
Assistant Professor
University of Texas Medical Branch School of Nursing
1100 Mechanic St, Galveston, TX 77550
Email: amgarey@utmb.edu
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