Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review

Area of Science:

• Healthcare Informatics
• Data Science Applications
• Nursing Research

Background:

• Healthcare generates vast datasets, necessitating advanced analytics.
• Nurse leaders face challenges keeping pace with evolving data science.
• Data science offers potential to improve clinical processes and patient outcomes.

Purpose of the Study:

• To conduct a scoping literature review of data science applications in nursing-relevant phenomena.
• To identify trends and gaps in data science research published in 2019.
• To inform nurse leaders about data science's utility and research frontiers.

Main Methods:

• Scoping literature review methodology.
• Analysis of papers published in 2019.
• Categorization of data science applications across 15 nursing phenomena.

Main Results:

• 14 of 15 nursing phenomena were addressed in 2019 data science literature.
• Commonly explored phenomena include readmissions and pressure injuries.
• Contemporary methods like natural language processing and neural networks were identified.
• Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were underrepresented.

Conclusions:

• Data science is increasingly utilized in healthcare for nursing-relevant issues.
• Significant research exists on readmissions and pressure injuries.
• Gaps in literature highlight areas for future data science research in nursing, particularly concerning AI/ML acceptance, burnout, patient safety, and unit culture.