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A Framework and Method for Measuring the Implementation of Data Science in Critical Care
Daniel Woznica1, Tamara Al-Hakim1, Vishakha K Kumar1
1Society of Critical Care Medicine, Mount Prospect, IL.
Background:
The implementation of data science concepts, skills, and tools in critical care research and practice faces multiple, complex barriers.
Methods:
We developed an implementation science-based framework and method for measuring the adoption, implementation, and sustainment of data science concepts, skills, and tools in critical care-the Society of Critical Care Medicine (SCCM) Discovery Data Science Campaign (DSC) Implementation Research Logic Model (IRLM). Our IRLM specifies constructs for: 1) key determinants (i.e., barriers and facilitators) influencing the implementation of data science concepts, skills, and tools in critical care; 2) implementation strategies deployed by the SCCM Discovery DSC to address these determinants; 3) theorized mechanisms of action by which these strategies affect outcomes; and 4) upstream and downstream implementation outcomes influenced by implementation strategies.
Results And Conclusions:
We believe that our model can facilitate more rigorous measurement of theoretically grounded, empirically assessable factors driving implementation of data science concepts, skills, and tools in critical care.
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