1. Delaney, Connie W. PhD, RN, FAAN, FACMI, FNAP
  2. Weaver, Charlotte PhD, RN, MSPH, FAAN

Article Content

On June 13-15, 2018, in Minneapolis, we witnessed the sixth annual Nursing Knowledge: Big Data Science (NKBD) conference. Just as the original 2013 invitational initiative that attracted nursing professionals from across practice, government policy, software vendors, professional organizations, informatics, academia, and research, 2018's NKBD open conference showed the same diversity as well as growth in numbers. The NKBD Initiative's core mission across these 6 years has stayed the same: to develop a roadmap for achieving "sharable and comparable" nursing data and to ensure the timely adoption of big data methodologies across all of nursing's domains.


The 2018 conference kicked off with a powerful set of preconference workshops addressing key work group areas of high-value topics. This year's topics were Hands-On, Full Life Cycle Data Science; Social Media and Mobile Health Analytics; and Streamlining/Transforming EHR Documentation. Conducted so that participants can do hands-on work with new tools or demonstration case studies that give "how to" information, these 3-day-long sessions were jam-packed. The preworkshop evaluations rated the applied learning value of all three sessions as "very high," with requests for more of the same going forward.


The structure of the NKBD conference is primarily to provide a platform for the 10 working groups to report on their accomplishments over the past year.1 This year's reports demonstrated the considerable evolution that has occurred over these past 6 years and the significant body of work completed and breadth of accomplishments achieved from the 10 working groups.2 A list of some of the publications generated across the working groups is attached at the end of this article, and a full review is included in conference proceedings.3 The breadth of the work spans terminology standardization work under Matney/Settergren et al; data analytics and information modeling, Sylvia/Westra et al; nursing value, Welton/Harper et al; and nursing informatics curriculum standards and resources, Wilson/Manos et al, to list just a few.


During the body of the main conference, time allowed working groups to do on-the-ground work, as well as networking and collaborations with other work groups. In a summary session on the afternoon of the last day, work group chairs reported on their go-forward strategies, and this detail can be reviewed in the Resource Center site and conference proceedings.2,3


Rebecca Freeman and Kelly Cochrane gave a current state update on "Nursing and Health IT" including go-forward plans on the joint initiative between the Office of the National Coordinator and the American Nurses Association on transforming nursing EHR clinical documentation.2,3 In addition, two keynotes expanded dialogue into interprofessional practice and education, and social media. Barbara Brandt, PhD, FNAP, Director of the National Center for Interprofessional Practice and Education, presented work being done on the development of the inaugural national Interprofessional Practice and Education Information Exchange and essential core data set. Anne Pryor, MS, a certified Online Visibility Strategist with LinkedIn, shared multiple LinkedIn tools and bold strategies to increase visibility of the individual professional work, expanded networks, and the innovative Web/LinkedIn platform supporting the NKBD Initiative.


We invite all of you who have an interest in any of the working group topics to reach out to their chairs and to join in their work throughout this coming year. The networking and learning from active participation in a working group are very valuable for the applied knowledge gained. We hope that you will save the date for next year's NKBD conference in Minneapolis, June 5-7, 2019! First-time attendees and students are most welcome.




1. University of Minnesota School of Nursing. Big Data: Empowering Health topics (workgroups).[Context Link]


2. University of Minnesota School of Nursing. Big Data: Empowering Health resource center.[Context Link]


3. University of Minnesota School of Nursing. NKBD 2018 conference proceedings.[Context Link]

Addendum: Working Groups 2017/2018 Publications


1. Abhyankar S, Vreeman D, Westra BL, Delaney CW. Letter to the editor-comments on the use of LOINC and SNOMED CT for representing nursing data. International Journal of Nursing Knowledge. 2018;2: 82-85.


2. Ariosto D, Harper E, Wilson ML, Hull SC, Nahm ES, Sylvia M. Population health: a nursing action plan. Open Journal of American Medical Informatics. 2018.


3. Arons A, DeSilvey S, Fichtenberg C, Gottlieb L. Improving the interoperability of social determinants data in electronic health records (working paper). UCSF SIREN. 2017.


4. D'Agostino F, Vellone E, Welton JM. Nursing diagnoses and interventions and their relationship with outcomes: a prospective study using a Nursing Minimum Data Set in an oncology hospital setting. Cancer Nursing. 2018.


5. D'Agostino F, Vellone E, Welton JM. Prevalence of nursing diagnoses as a measure of nursing complexity in a hospital setting. Journal of Advanced Nursing. 2017;73(9): 2129-2142.


6. Delaney CW, Weaver CA, Warren JJ, Clancy TR, Simpson RL. Big Data-Enabled Nursing: Education, Research and Practice. Cham, Switzerland: Springer International Publishing; 2017.


7. Gao G, Maganti S, Monsen KA. Older adults, frailty, and the social and behavioral determinants of health. Big Data and Information Analytics. 2017;1-12.


8. Garcia A. Variability in acuity in acute care. The Journal of Nursing Administration. 2017;47(10): 476-483.


9. Garcia A, Lovett R. Using NOC to determine staffing needs. In: Moorhead S, Johnson M, Maas M, eds. Nursing Outcomes Classification. St Louis, MO: Elsevier; 2018: 29-35.


10. Hewner S, Casucci S, Pratt R, et al. Integrating social determinants of health into primary care clinical and informational workflow during care transitions. 2017;5(2): 2.


11. Hewner S, Sullivan S, Yu G. Reducing emergency room visits and in-hospitalizations by implementing best practice for transitional care using innovative technology and big data. Worldviews on Evidence-Based Nursing. 2018;15(3): 170-177.


12. Jenkins P, Garcia A, Farm-Franks D, Choromanski L, Welton JM. Academic/practice/industry collaboration to develop nursing value research data warehouse governance. Nursing Economics.


13. Moon L, Harper E, Welton J, Clancy G. Nursing value user stories: a method for linking value measurement of nurse contribution to patient outcomes. Computers, Informatics, Nursing. 2018.


14. Procter PM, Wilson ML. Nursing, professional curiosity and big data co-creating ehealth. Studies in Health Technology and Informatics. 2018;247: 186-190.


15. Welton JM, Harper EH. Case study 5.1: value-based nursing care model development. In: Delaney CAW CW, Warren JJ, Clancy TR, Simpson RL, eds. Big Data-Enabled Nursing: Education, Research, and Practice. Cham, Switzerland: Springer International Publishing; 2017:95-101.


16. Welton JM. Measuring patient acuity: implications for nurse staffing and assignment. The Journal of Nursing Administration. 2017;47(10): 475.


17. Welton JM, Kleiner C, Adrian B. Time-referenced data. Nursing Economics. 2017;35(3): 150-151.


18. Welton JM, Jenkins P, Perraillon M. Microcosting models in nursing. Nursing Economics. 2018;36(1): 46-49.


19. Welton JM, Harper EH. Using NOC to measure nursing care value in clinical practice. In: Morehead EAS S, Johnson M, Maas ML, eds. Nursing Outcomes Classification (NOC): Measurement of Health Outcomes. St Louis, MO: Elsevier; 2018: 29, 45-47.


20. Welton JM, Kleiner C, Valdez C, Richardson S, Boyle K, Lucas E. Using time-referenced data to assess medication administration performance and quality. The Journal of Nursing Administration. 2018;48(2):100-106.


21. Westra BL, Christie B, Johnson SG, et al. Modeling flowsheet data to support secondary use. Computers, Informatics, Nursing. 2017;39(9):452-458.


22. Westra BL, Sylvia M, Weinfurter EF, et al. Big data science: a literature review of nursing research exemplars. Nursing Outlook. 2017;65: 549-561.


23. Westra BL, Johnson SG, Ali S, et al. Validation and refinement of a pain information model from EHR flowsheet data. Applied Clinical Informatics 2018;9: 185-198.


24. Wilson ML, Proctor PM. Exploring community planning thinking as a model for use case development. Studies in Health Technology and Informatics. 2016;225: 1020-1021.