Implementation Science at UW

Research Methods in Implementation Science

A broad and inclusive definition of the field defines implementation science as:

A systematic, scientific approach to ask and answer questions about how we get “what works” to people who need it, for as long as they need it, with greater speed, fidelity, efficiency, quality and relevant coverage.

This broad landscape of inquiry allows for the application of at least ten research methods from a wide range of disciplines. These methods are used in order to understand and improve the determinants, processes, and outcomes of implementation and, ultimately, scale-up and sustainability to achieve population-level health benefits.

While this selection of research methods is not exhaustive, together they provide a set of tools that are used to assess and improve implementation and scale-up of health interventions.

Graphic depicting the 10 main research methods used in implementation science.

These methods allow exploration of the continuum consisting of three categories of outcomes:

  • proximal implementation outcomes – acceptability, appropriateness, adoption, costs, feasibility, fidelity, penetration, and sustainability
  • intermediate service delivery outcomes – efficiency, equity, timeliness, and patient-centeredness
  • distal health outcomes
Open Access articles will be marked with ✪
Please note some journals will require subscriptions to access a linked article.

Definition: The Systems Analysis and Improvement Approach (SAIA) is an evidence-based implementation strategy that combines system engineering tools into a five-step, facility-level package to give clinic staff and managers a system-wide view of their cascade performance, identify priority areas for improvement, discern modifiable opportunities for improvement, and test workflow modifications. The process is iterative, which means health care teams can continue to use the package to further improve care and respond to new bottlenecks that arise. Visit to learn more about this method and about projects using SAIA currently.

Examples of use:

Definition: Routine surveillance data from control and experimental groups can be used illustrate the performance or impact of new policies and programs in an environment.

Examples of use: