Integrating implementation science and intervention optimization

Implementation scientists increasingly recognize the value of multiple strategies to improve the adoption, fidelity, and scale up of an evidence-based intervention (EBI). However, with this recognition comes the need for alternative and innovative methods to ensure that the package of implementation strategies work well within constraints imposed by the need for affordability, scalability, and/or efficiency. The aim of this article is to illustrate that this can be accomplished by integrating principles of intervention optimization into implementation science.

We use a hypothetical example to illustrate the application of the multiphase optimization strategy (MOST) to develop and optimize a package of implementation strategies designed to improve clinic-level adoption of an EBI for smoking cessation.

We describe the steps an investigative team would take using MOST for an implementation science study. For each of the three phases of MOST (preparation, optimization, and evaluation), we describe the selection, optimization, and evaluation of four candidate implementation strategies (e.g., training, treatment guide, workflow redesign, and supervision). We provide practical considerations and discuss key methodological points.

Our intention in this methodological article is to inspire implementation scientists to integrate principles of intervention optimization in their studies, and to encourage the continued advancement of this integration.