Combining Qualitative Interviewing with Systems Science to Understand How Practice Facilitators Tailor Implementation Support to Context

ERIN KENZIE, PhD
Oregon Health & Science University

A complex array of factors affect the ability of primary care clinics to successfully integrate evidence-based practices into routine care. Models like i-PARIHS (integrated Promoting Action on Research Implementation in Health Services) identify factors related to the intervention, recipients (motivation, skill), and multiple levels of context including local (workflows, past experience), organization (culture, structure), and external (policy drivers). To effectively support clinics, practice facilitators-individuals trained to build the capacity of primary care practices-must accurately assess clinics’ needs and identify corresponding means of implementation support. Examining how this tailoring happens is key to evaluating program outcomes and maximizing program success.

This research is being conducted as part of the ANTECEDENT study, an AHRQ-funded EvidenceNOW unhealthy alcohol use project led by the Oregon Rural Practice-based Research Network (ORPRN). In ANTECEDENT, ORPRN practice facilitators provide technical assistance and supportive services to primary care clinics to adopt or improve evidence-based methods of addressing unhealthy alcohol use through screening, brief intervention, and medication assisted treatment (MAT). Efforts are aligned with the state’s Medicaid quality incentive metric for SBIRT (screening, brief intervention, and referral to treatment) and in partnership with SBIRT Oregon (www.sbirtoregon.org).

In this mixed methods evaluation, we combine qualitative interviews with causal-loop diagramming, a systems science method for describing complex interrelationships. This poster will outline how we are using causal-loop diagramming to enhance our qualitative analysis and structure our understanding of how practice facilitators respond to clinic needs. We will describe our approach for generating causal-loop diagrams illustrating practice facilitators’ mental models of practice change from qualitative interviews.

Preliminary results from baseline analyses will be presented by illustrating causal-loop diagrams of practice facilitators’ mental models of practice change and tailoring implementation support to context. By analyzing the structure and content of the diagrams, insight can be gained about the range of perspectives held by practice facilitators. Strengths and limitations of this approach to modeling from qualitative data will be identified.

System dynamics, and causal-loop diagramming in particular, is well suited for enhancing qualitative analysis. Our novel approach provides a framework to specify documented or assumed cause-and-effect relationships. This approach can illustrate the mental models of practice facilitators or researchers and help improve evaluation as well as implementation outcomes.