Decision-based models of the implementation of interventions in systems of healthcare: Implementation outcomes and intervention effectiveness in complex service environments

Autoři: Arno Parolini aff001;  Wei Wu Tan aff001;  Aron Shlonsky aff002
Působiště autorů: Department of Social Work, University of Melbourne, Carlton, Victoria, Australia aff001;  Department of Social Work, Monash University, Caulfield East, Victoria, Australia aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223129


Implementation is a crucial component for the success of interventions in health service systems, as failure to implement well can have detrimental impacts on the effectiveness of evidence-based practices. Therefore, evaluations conducted in real-world contexts should consider how interventions are implemented and sustained. However, the complexity of healthcare environments poses considerable challenges to the evaluation of interventions and the impact of implementation efforts on the effectiveness of evidence-based practices. In consequence, implementation and intervention effectiveness are often assessed separately in health services research, which prevents the direct investigation of the relationships of implementation components and effectiveness of the intervention. This article describes multilevel decision juncture models based on advances in implementation research and causal inference to study implementation in health service systems. The multilevel decision juncture model is a theory-driven systems approach that integrates structural causal models with frameworks for implementation. This integration enables investigation of interventions and their implementation within a single model that considers the causal links between levels of the system. Using a hypothetical youth mental health intervention inspired by published studies from the health service research and implementation literature, we demonstrate that such theory-based systems models enable investigations of the causal pathways between the implementation outcomes as well as their links to patient outcomes. Results from Monte Carlo simulations also highlight the benefits of structural causal models for covariate selection as consistent estimation requires only the inclusion of a minimal set of covariates. Such models are applicable to real-world context using different study designs, including longitudinal analyses which facilitates the investigation of sustainment of interventions.

Klíčová slova:

Decision making – Economic models – Health services research – Mental health and psychiatry – Monte Carlo method – Psychotherapy – Statistical data – Complex systems


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