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Integrating implementation science and intervention optimization.

Kate Guastaferro1, Corrina Moucheraud2, Jonathan Purtle2

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Summary
This summary is machine-generated.

This study introduces intervention optimization principles to implementation science. It demonstrates how the multiphase optimization strategy (MOST) can develop efficient and scalable implementation strategies for evidence-based interventions.

Keywords:
Factorial experimentIntervention optimizationMultiphase optimization strategy (MOST)

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Area of Science:

  • Implementation Science
  • Health Services Research
  • Intervention Optimization

Background:

  • Recognizes the need for cost-effective implementation strategies for evidence-based interventions (EBIs).
  • Highlights the challenge of balancing strategy effectiveness with affordability, scalability, and efficiency.

Purpose of the Study:

  • To illustrate the integration of intervention optimization principles into implementation science.
  • To demonstrate the application of the multiphase optimization strategy (MOST) for developing and optimizing implementation strategies.

Main Methods:

  • Utilizes a hypothetical example to showcase the multiphase optimization strategy (MOST).
  • Details the three phases of MOST: preparation, optimization, and evaluation.
  • Examines the selection, optimization, and evaluation of four candidate implementation strategies.

Main Results:

  • Provides a step-by-step guide for applying MOST in implementation science research.
  • Discusses practical considerations and key methodological points for using MOST.
  • Illustrates the optimization of strategies like training, treatment guides, workflow redesign, and supervision.

Conclusions:

  • Encourages implementation scientists to adopt intervention optimization principles.
  • Aims to inspire further advancements in integrating optimization into implementation science.