Leveraging machine learning approach to identify relationships between practice facilitation strategies and practice characteristics based on the implementation research logic model.

backgroundMachine learning (ML)-a field of study dedicated to the principled extraction of knowledge from complex data-can benefit implementation science, quality improvement (QI), and primary care research. Given the general complexity of implementation research and the need to develop strategies for understanding relationships among practice characteristics and practice facilitation strategies, we chose the Implementation Research Logic Model (IRLM) as an underlying structure for the data and to identify relationships that might be associated with outcomes. This study illustrates this novel method involving ML and an IRLM in the context of a practice facilitation-supported QI program in primary care.

methodsWe applied advanced statistical methods within a machine learning framework to data from the Healthy Hearts in the Heartland (H3) study, including practice facilitation data and practice and staff participation survey, to assess the relationship between practice attributes and practice facilitator strategies and their impact on successful implementation of QI interventions. We used PCA for feature selection, incorporated practice facilitators' knowledge for contextual factor validation, and employed Structural Equation Modeling (SEM) to analyze relationships among contextual factors, latent variables, practice facilitation strategies, and outcomes.

resultsWe selected 20 contextual factors and identified practice facilitation strategies and mapped them to the IRLM. Cronbach's alphas of contextual factors in the five domains (Intervention characteristics, outer setting, inner setting, characteristics of individuals, and implementation process) are 0.71, 0.82, 0.72, 0.89, 0.86, respectively. We used structural equation modeling to analyze the relationships among contextual factors, latent variables, practice facilitation strategies (Doing Tasks, Project Management, Consulting, Teaching, and Coaching), and outcomes (number of implemented QI interventions and Change Process Capability Questionnaire (CPCQ) score). All five facilitation strategies had statistically significant associations with the implementation of QI interventions (all P < 0.05).

conclusionsThe combination of ML and the theory behind the IRLM can be used to identify relationships between inner and outer context determinants and implementation strategies and study outcomes in pragmatic research study datasets. All the proposed strategies in H3 were statistically associated with completed QI interventions; and the strategies had more impact on the implementation of interventions than CPCQ change. By understanding the relationship between outcomes, practice determinants and coaching strategies, practice facilitators can better help primary care practices adapt and implement interventions and build capacity to adapt to change.