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

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Implementing data science in critical care faces barriers. A new framework, the SCCM Discovery Data Science Campaign (DSC) Implementation Research Logic Model (IRLM), measures adoption, implementation, and sustainment of these tools.

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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.