Data Science and Graduate Nursing Education: A Critical Literature Review. Issue 3 (May 2020)

• Record Type: Journal Article
• Authors: Foster, Marva; Tasnim, Zarin
• Is Part Of: Clinical nurse specialist. Volume 34:Issue 3(2020)
• Publication Date: 2020-05
• Subjects: big data -- data science -- graduate education -- nurse; Nurse practitioners -- United States -- Periodicals; Nursing -- Practice -- Periodicals; 610.7305
• DOI: 10.1097/NUR.0000000000000516
• Languages: English
• ISSNs: 0887-6274

Abstract

Abstract : Background: The emergence of big data and data science offers unprecedented opportunities for accelerating scientific advances in nursing, yet current nursing curricula are not adequate to prepare students to leverage those opportunities. Purpose: The purpose of this review was to describe current strategies that can be used to educate graduate nurses about data science methods as well as facilitators and challenges to adopting those strategies. Method: We conducted a critical literature review of papers addressing data science and graduate nursing education. Results: Ten articles were included in this review. The most common strategy was the integration of data science methods into existing courses throughout the graduate nursing curricula. A major facilitator was interdisciplinary collaboration between nursing faculty and colleagues in other disciplines. Conclusion: The findings provide strategies that can be used to prepare graduate nurses to work in data science teams to shape big data research and optimize patient outcomes.