Nursing Needs Big Data and Big Data Needs Nursing
Abstract
PURPOSE: Contemporary big data initiatives in health care will benefit from greater integration with nursing science and nursing practice; in turn, nursing science and nursing practice has much to gain from the data science initiatives. Big data arises secondary to scholarly inquiry (e.g., -omics) and everyday observations like cardiac flow sensors or Twitter feeds. Data science methods that are emerging ensure that these data be leveraged to improve patient care. ORGANIZING CONSTRUCT: Big data encompasses data that exceed human comprehension, that exist at a volume unmanageable by standard computer systems, that arrive at a velocity not under the control of the investigator and possess a level of imprecision not found in traditional inquiry. Data science methods are emerging to manage and gain insights from big data. METHODS: The primary methods included investigation of emerging federal big data initiatives, and exploration of exemplars from nursing informatics research to benchmark where nursing is already poised to participate in the big data revolution. We provide observations and reflections on experiences in the emerging big data initiatives. CONCLUSIONS: Existing approaches to large data set analysis provide a necessary but not sufficient foundation for nursing to participate in the big data revolution. Nursing's Social Policy Statement guides a principled, ethical perspective on big data and data science. There are implications for basic and advanced practice clinical nurses in practice, for the nurse scientist who collaborates with data scientists, and for the nurse data scientist. CLINICAL RELEVANCE: Big data and data science has the potential to provide greater richness in understanding patient phenomena and in tailoring interventional strategies that are personalized to the patient.
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There is a growing, but still a limited critical discourse in the nursing literature.
“Notwithstanding these important implications of AI for the nursing profession, there is a growing, but still a limited critical discourse in the nursing literature (Brennan & Bakken, 2015; Linnen et al., 2019).”
From Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative (2021) · cites this paper as Brennan & Bakken, 2015
Epic and Microsoft announced a partnership to incorporate generative AI into electronic health records.
“the electronic health record (EHR) vendor, Epic, and Microsoft recently announced a partnership to incorporate generative AI into EHRs.11”
From Increasing Generative Artificial Intelligence Competency among Students Enrolled in Doctoral Nursing Research Coursework (2024) · cites this paper as [11]
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Womack, D. M., Hribar, M. R., Steege, L. M. B., Vuckovic, N. H., Eldredge, D. H., & Gorman, P. (2020) · Applied Clinical Informatics · doi:10.1055/s-0040-1715829 · Cited by 10 · see citing works
Glanville, D., Hutchinson, A., & Khaw, D. (2022) · Journal of Medical Internet Research · doi:10.2196/39987 · Cited by 11 · see citing works
Turchioe, M. R., Kisselev, S., Fan, R., & Henry, S. B. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae208 · Cited by 10 · see citing works
The Robert Wood Johnson Foundation Future of Nursing Scholars program: The scholar experience
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Evolving Role of the Nursing Informatics Specialist
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Standardized Nursing Data and the Oncology Nurse
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Ameel, M., Kontio, R., & Junttila, K. (2019) · Journal of Advanced Nursing · doi:10.1111/jan.14127 · Cited by 8 · see citing works
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Fernandes, F., Shinners, L., Mota, M., Santos, P., & Sá, L. (2026) · Nursing and Health Sciences · doi:10.1111/nhs.70308 · Cited by 12 · see citing works
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[Retracted] Optimization of Clinical Nursing Management System Based on Data Mining
Chen, Y. (2021) · Complexity · doi:10.1155/2021/2110154 · Cited by 7 · see citing works
RetractedFeasibility of Representing Data from Published Nursing Research Using the OMOP Common Data Model.
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Corwin, E. J., Jones, D. P., & Dunlop, A. L. (2018) · Journal of Nursing Scholarship · doi:10.1111/jnu.12446 · Cited by 7 · see citing works
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Ameri, F., Golaghaie, F., Harorani, M., Poorcheraghi, H., Rahbarian, A., & Jadidi, A. (2018) · Journal of Client-centered Nursing Care · doi:10.32598/jccnc.4.4.185 · Cited by 7 · see citing works
Association of Nurse Staffing With Nosocomial Infections of Very Low-Birth-Weight Infants
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Information-Seeking Behavior of Greek Nursing Students
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Barton, A. J. (2016) · Journal of Nursing Education · doi:10.3928/01484834-20160216-01 · Cited by 7 · see citing works
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- Authors
- Brennan, P. F., & Bakken, S.
- Affiliations
- <i>Beta Eta at large</i>, Lillian S. Moehlman‐Bascom Professor of Nursing and Industrial Engineering University of Wisconsin‐Madison Madison WI USA
- <i>Alpha Eta</i>, The Alumni Professor of Nursing an Professor of Biomedical Informatics Columbia University New York NY USA
- Venue
- Journal of Nursing Scholarship
- Publisher
- Wiley
- Published
- 2015-08-19
- Volume
- 47
- Issue
- 5
- Pages
- 477-484
- ISSN
- 0363-2792 (ISSN-L)
- 1527-6546
- 1547-5069
- OpenAlex
- W1518948029
- Cited by
- 199
- References
- 24
- Citations by year
- 2026: 11 · 2025: 9 · 2024: 12 · 2023: 20 · 2022: 11 · 2021: 25 · 2020: 23 · 2019: 16 · 2018: 23 · 2017: 20 · 2016: 28 · 2015: 1
- FWCI
- 47.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Nursing Scholarship (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (1343969)
- Agency for Healthcare Research and Quality (R01HS02254, R01HS02296)
- National Institute of Nursing Research (R01NR14430)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Keywords
- big data, data science, nursing science, nursing informatics, nurse researchers, individualized care, nursing practice, big data ethics
- MeSH terms
- Humans, Nursing Research, Nursing Informatics, Data Mining
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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