Abstract
Nurse leaders working with large volumes of interdisciplinary healthcare data are in need of advanced guidance for conducting analytics to improve population outcomes. This article reports the development of a roadmap to help nursing leaders use data science principles and tools to inform decision-making, thus supporting research and approaches in clinical practice that improve healthcare for all. A consensus-building and iterative process was utilized based on the Cross-Industry Standard Process for Data Mining approach to big data science. Using the model, a set of components are described that combine and achieve a process for data science projects applicable to healthcare issues with the potential for improving population health outcomes. The roadmap was tested using a workshop format. The workshop was presented to two audiences: nurse leaders and informatics/healthcare leaders. Results were positive and included suggestions for how to further refine and communicate the roadmap.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 484-489 |
| Number of pages | 6 |
| Journal | CIN - Computers Informatics Nursing |
| Volume | 38 |
| Issue number | 10 |
| DOIs | |
| State | Published - 2020 |
Bibliographical note
Funding Information:This project was funded by a University of Minnesota School of Nursing Foundation grant. Corresponding author: Lisiane Pruinelli, PhD, RN, FAMIA, School of Nursing, University of Minnesota, 308 Harvard Street SE, Minneapolis, MN, 55455 (pruin001@umn.edu). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.
Publisher Copyright:
© Lippincott Williams & Wilkins.
Keywords
- CRISP-DM
- Data science
- Nurse leaders
- Nursing education
- Nursing informatics
PubMed: MeSH publication types
- Journal Article