ABSTRACT
Background: Efforts to maximize the impact of healthcare improvement interventions are hampered when intervention components are not well defined or described, precluding the ability to understand how and why interventions are expected to work. Method: We partnered with two organizations delivering province-wide quality improvement interventions to establish how they envisaged their interventions lead to change (their underlying causal assumptions) and to identify active ingredients (behavior change techniques [BCTs]). The interventions assessed were an audit and feedback report and an academic detailing program. Both focused on supporting safer opioid prescribing in primary care in Ontario, Canada. Data collection involved semi-structured interviews with intervention developers (n = 8) and a content analysis of intervention documents. Analyses unpacked and articulated how the interventions were intended to achieve change and how this was operationalized. Results: Developers anticipated that the feedback report would provide physicians with a clear understanding of their own prescribing patterns in comparison to others. In the feedback report, we found an emphasis on BCTs consistent with that assumption (feedback on behavior; social comparison). The detailing was designed to provide tailored support to enable physicians to overcome barriers to change and to gradually enact specific practice changes for patients based on improved communication. In the detailing materials, we found an emphasis on instructions on how to perform the behavior, for a range of behaviors (e.g., tapering opioids, treating opioid use disorder). The materials were supplemented by detailer-enacted BCTs (e.g., social support [practical]; goal setting [behavior]; review behavioral goal[s]). Conclusions: The interventions included a small range of BCTs addressing various clinical behaviors. This work provides a methodological example of how to apply a behavioral lens to surface the active ingredients, target clinical behaviors, and causal assumptions of existing large-scale improvement interventions that could be applied in other contexts to optimize effectiveness and facilitate scale and spread.
What is already known about the topic?: The causal assumptions and key components of implementation interventions are often not well described, which limits the influence of implementation science on implementation practice. What does this paper add?: This work provides an approach for surfacing the causal assumptions from intervention developers (through interviews with eight participants) and active ingredients from intervention materials, focusing on two real-world interventions already delivered at scale and designed to promote safer opioid prescribing. The analysis provides a comprehensive intervention description and reveals the extent to which final interventions align with developers' intentions. What are the implications for practice, research, or policy?: The findings provide a foundation for future work which will describe the effectiveness of these interventions (alone and in combination) and explore whether they achieve change in the intended ways, thereby providing an example of a more fulsome intervention evaluation. More broadly, our methods can be used by implementation practitioners to review and reflect on their intervention development process and support comprehensive intervention descriptions.
