Causal Bayesian networks to quantify the interactions that influence implementation success

Despite the potential of evidence-based medical innovations to improve patient outcomes, their integration remains difficult. Implementation science aims to assist by identifying and deploying effective implementation strategies within complex health care settings. Determinant frameworks, such as the Consolidated Framework for Implementation Research (CFIR), help identify factors influencing implementation success but do not specify mechanisms or methods for selecting optimal strategies. Selection methods are largely empirical, highlighting the need for objective, quantifiable approaches. We developed causal Bayesian networks (BNs) to model the interdependencies amongst contextual factors, determinants and outcomes with a specific example: the detection and management of chonic wet cough in Indigenous Australian children in primary health care settings. The BNs, informed by CFIR domains and prior qualitative research, quantifies the impact of barriers and enablers on implementation outcomes. The BNs enable predictions of intervention effects, and the assessment and quantification of potential implementation strategies, or a combination of strategies. The BNs are linked to a simple survey that allows implementation strategies to be tailored for each setting and that was administered at several sites across Australia to validate the models. The overall process, including the BNs and surveys, constitutes a generalisable structured workflow for selecting the most promising strategies. We describe the model development and validation, and the broader applicability of our BN-based workflow in implementation science.