A Framework and Method for Measuring the Implementation of Data Science in Critical Care.

Area of Science:

• Critical care medicine
• Data science
• Implementation science

Background:

• Significant barriers hinder the adoption of data science concepts, skills, and tools in critical care research and practice.
• Addressing these barriers is crucial for advancing critical care.
• Data science offers transformative potential for critical care.

Purpose of the Study:

• To develop and present a framework for measuring the implementation of data science in critical care.
• To introduce the Society of Critical Care Medicine (SCCM) Discovery Data Science Campaign (DSC) Implementation Research Logic Model (IRLM).
• To provide a theoretically grounded and empirically assessable model for understanding implementation drivers.

Main Methods:

• Developed an implementation science-based framework and method.
• The framework is named the SCCM Discovery DSC Implementation Research Logic Model (IRLM).
• The IRLM specifies constructs for determinants, strategies, mechanisms of action, and outcomes.

Main Results:

• The IRLM outlines key determinants (barriers and facilitators) impacting data science implementation.
• It details implementation strategies used by the SCCM Discovery DSC.
• The model connects strategies to mechanisms of action and implementation outcomes.

Conclusions:

• The developed IRLM can enhance the measurement of data science implementation in critical care.
• It facilitates rigorous, theoretically grounded, and empirically assessable evaluations.
• This model supports the successful integration of data science into critical care settings.