An Applied Healthcare Data Science Roadmap for Nursing Leaders: A Workshop Development, Conceptualization, and Application

Lisiane Pruinelli, Steven G. Johnson, Brianna Fesenmaier, Tamara J. Winden, Cynthia Coviak, Connie W. Delaney

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 languageEnglish (US)
Pages (from-to)484-489
Number of pages6
JournalCIN - Computers Informatics Nursing
Volume38
Issue number10
DOIs
StatePublished - 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.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • CRISP-DM
  • Data science
  • Nurse leaders
  • Nursing education
  • Nursing informatics

PubMed: MeSH publication types

  • Journal Article

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