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Consolidated Framework for Implementation Research.

Lowery JC, Damschroder LJ. Consolidated Framework for Implementation Research. Paper presented at: Canadian Knowledge Transfer and Exchange Community of Practice Meeting; 2012 Apr 23; Toronto, Canada.




Abstract:

This presentation will demonstrate the use of constructs from the Consolidated Framework for Implementation Research (CFIR) in analyzing data from a post-implementation formative evaluation of VA's Tele-retinal Imaging Program for screening for diabetic retinopathy (DR). The analysis answered the question: What are the important program, organizational, individual, and process differences between facilities with high rates of screening and follow-up ("high program uptake"), and those with low rates of screening and follow-up ("low program uptake")? The formative evaluation was conducted in nine facilities, selected on facility variability in percentages of patients screened for DR using the tele-retinal screening program. Data were collected through semi-structured phone interviews with 42 participants from across the nine facilities, including program Coordinators, Readers, Imagers, and Nurse Managers involved in the program. After coding the interviews deductively according to CFIR constructs, an ordinal value (-2, -1, 0, 1, or 2) was assigned to each construct at each site. These values represent the perceived magnitude and role of each construct in the implementation of the program. These data were then used to construct a matrix for identifying correlations between each construct and program uptake. Determination of correlations between the ordinal data and degree of program uptake was then used to identify those constructs that most likely influenced implementation effectiveness. Identification of the key constructs affecting implementation is critical for making recommendations for improving dissemination of a program. Specific recommendations were identified by going back to the qualitative data, to determine how each construct was operationalized at the sites. These data can also contribute to implementation science, by providing specific evidence for each construct's role in implementation.





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