Multi-level Adaptive Implementation Strategies (MAISYs):
Design Principles, Optimization Questions and Choosing the Right Experimental Design

Daniel Almirall, PhD
University of Michigan

ABSTRACT:
Evidence-based practices often fail to be implemented or sustained due to barriers at multiple levels of an organization (e.g., system-level, practitioner-level).  A growing cadre of implementation strategies can help mitigate challenges at these multiple levels, but significant heterogeneity exists in whether, and to what extent, organizations—and the practitioners who deliver treatment within them—respond to different strategies. However, it is impractical to provide all (or even most) of these strategies to all levels, at all times.  This suggests the need for an approach that sequences and adapts the provision of implementation strategies to the changing context and needs of practitioners within the multiple levels of an organization. A multi-level adaptive implementation strategy (MAISY) offers a replicable, approach to precision implementation that guides implementers in how best to adapt and re-adapt (e.g., augment, intensify, switch) implementation strategies based on the changing context and changing needs at multiple levels. 

In this manuscript, we present: (1) a set of principles for the design of MAISYs, (2) different types of scientific questions whose answers could lead to the construction of optimized MAISYs, and (3) different types of randomized trial designs and how best to match them to the optimization questions of interest. The latter includes a new experimental design—the Multilevel Implementation Sequentially, Randomized Multiple Assignment Randomized Trials (MI-SMART), in which a sequence of randomizations occur at multiple levels. To illustrate ideas, we draw on two studies where the goal is to develop an optimized MAISY that leads to the adoption of evidence-based practices in school settings.

LEARNING OBJECTIVES

 Individuals who attend this presentation will:

 1.  Learn about Multilevel Adaptive Implementation Strategies (MAISYs) and basic MAISY design principles.

2.   Learn about a variety of novel scientific questions whose answers can be used to construct an optimized MAISY.

3.   Learn about different types of optimization trial (experimental) designs and how to match the right trial design with the scientific questions of interest

4.   Begin to understand the difference between the optimization and evaluation of MAISYs.