Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature

Area of Science:

• Nursing informatics
• Health services research
• Data science in healthcare

Background:

• Data science applications are increasingly used in healthcare, yet their implementation for nurse-sensitive indicators remains under-documented.
• Nurse-sensitive indicators are crucial for evaluating and improving patient care quality and outcomes.

Approach:

• A descriptive review of literature published in 2021 was conducted.
• 11 studies were analyzed for data science model development, implementation strategies, lessons learned, and challenges.
• Adherence to the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by AI (DECIDE-AI) framework was assessed.

Key Points:

• Data science systems were developed for adult populations, primarily in hospitals, targeting mortality, resource utilization, and infections.
• Development teams and stakeholders varied, with more focus on implementation methods than results.
• Most studies offered valuable lessons for future data science system implementations.

Conclusions:

• A significant gap exists in reporting the implementation of data science for nurse-sensitive indicators.
• Increased sharing of implementation strategies is essential for the successful adoption of these systems in clinical practice.