Using the Interactive Systems Framework to Support a Quality Improvement Approach to Dissemination of Evidence‐Based Strategies to Promote Early Detection of Breast Cancer: Planning a Comprehensive Dynamic Trial

Abstract

Dissemination efforts must optimize interventions for new settings and populations. As such, dissemination research should incorporate principles of quality improvement. Comprehensive Dynamic Trial (CDT) designs examine how information gained during dissemination may be used to modify interventions and improve performance. Although CDT may offer distinct advantages over static designs, organizing the many necessary roles and activities is a significant challenge. In this article, we discuss use of the Interactive Systems Framework for Dissemination and Implementation to systematically implement a CDT. Specifically, we describe "Bronx ACCESS", a program designed to disseminate evidence-based strategies to promote adherence to mammography guidelines. In Bronx ACCESS, the Intervention Delivery System will elicit information needed to adapt strategies to specific settings and circumstances. The Intervention Synthesis and Translation System will use this information to test changes to strategies through "embedded experiments". The Intervention Support System will build local capacities found to be necessary for intervention institutionalization. Simulation modeling will be used to integrate findings across systems. Results will inform on-going policy debate about interventions needed to promote population-level screening. More generally, this project is intended to advance understanding of research paradigms necessary to study dissemination.

A PRACTICAL IMPLEMENTATION SCIENCE HEURISTIC FOR ORGANIZATIONAL READINESS: R = MC2

“…The Support System uses strategies like training and TA to strengthen the Delivery System's ability to implement innovations with quality (Wandersman et al., ). The Synthesis and Translation System synthesizes the products of research and translates them into user‐friendly formats that can be easily accessed and understood by practitioners in the Support and Delivery Systems (Rapkin et al., ). There are bidirectional relationships between all three systems; they influence each other.…”

“…Relative advantage Degree to which a particular innovation is perceived as being better than what it is being compared against; can include perceptions of anticipated outcomes Armenakis et al, 1993;Damschroder et al, 2009;Hall & Hord, 2010;Rafferty et al, 2013;Rogers, 2003;SAMHSA, 2010;Schoenwald & Hoagwood, 2001;Weiner, 2009 Compatibility Degree to which an innovation is perceived at being consistent with existing values, cultural norms, experiences, and needs of potential users Chinman et al, 2004;Durlak & Dupre, 2008;Fetterman & Wandersman, 2005;Greenhalgh et al, 2004;Rafferty et al, 2013;Rogers, 2003;Simpson, 2002 Complexity Degree to which innovation is perceived as relatively difficult to understand and use Damschroder & Hagedorn, 2011;Fixsen et al, 2005;Greenhalgh et al, 2004;Meyers, Durlak & Wandersman, 2012;Rafferty et al, 2013;Wandersman et al, 2008. Trialability Degree to which an innovation can be tested and experimented with Armenakis et al, 1993;Greenhalgh et al, 2004;Rapkin et al, 2012;Rogers, 2003. Observability Degree to which outcomes that result from the innovation are visible to others Chinman et al, 2004;Damschroder et al, 2009;Ford et al, 2008;Rossi, Lipsey, & Freeman, 2004 Priority Extent to which the innovation is regarded as more important than others Armenakis et al, 1993;Damschroder et al, 2009;Klein, Conn, & Sorra, 2001 Table 2 presents a list of general capacities.…”

The “Retrofitting” Approach to Adapting Evidence-Based Interventions: A Case Study of Pediatric Asthma Care Coordination, United States, 2010–2014

“…One formal approach to doing this is Rapkin et al’s Comprehensive Dynamic Trial paradigm (11). This approach prescribes careful monitoring of the performance of interventions that are implemented in new settings.…”

“…From one perspective, they are undesirable deviations from a tried-and-true formula that lead to a voltage drop in the intervention’s efficacy (9,10). From another perspective, local tailoring of an EBI can enhance program outcomes by improving the fit of an intervention for a particular population or setting, thus allowing a process of evolution to optimize program functioning and effects (11) and fostering a sense of ownership among staff (12). …”

“…However, isolating the core elements that are both necessary and sufficient to achieve program effect across replications in diverse settings is often more art than science. For example, selecting core components is often based on the judgment of the program designers (10) rather than on resource-intensive systematic testing or experiential learning from replications (13), and some researchers suggest that even core program elements can be changed to beneficial effect in certain contexts (2,11). …”