Introduction to Implementation Science

Johns Hopkins University via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

7 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

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By the end of this course, learners will be able to distinguish implementation research from clinical effectiveness research; identify common implementation problems; use theories, models, and frameworks to assess barriers and facilitators; select and specify implementation strategies; choose pragmatic implementation outcomes and measures; and propose feasible study designs for evaluating implementation efforts in real-world settings. Through six instructional modules and a final synthesis module, this course introduces core concepts and methods in implementation science, with an emphasis on designing practical research that is useful for decision-makers, implementers, and communities. Learners will explore implementation science fundamentals, mixed-methods and hybrid designs, determinant and evaluation frameworks, implementation strategies, implementation outcomes and measurement, and randomized, pragmatic, and adaptive study designs. What makes this course unique is its applied, step-by-step structure. A running case study on integrated community case management in rural Madagascar, together with role-play labs and applied discussion prompts, guides learners through the process of moving from an implementation problem to a research question, determinant assessment, strategy bundle, measurement plan, and study design. Each module includes video lectures, core readings, quizzes, and opportunities to apply course concepts to real-world implementation challenges.

Syllabus

Foundations of Implementation Science
- This module introduces the field of implementation science and explains how implementation research differs from efficacy and effectiveness research. Learners will examine why evidence-based interventions often fail to achieve their expected impact in real-world settings, and how implementation research can help identify and address those gaps. The module emphasizes the importance of context, generalizability, stakeholder engagement, and careful description of settings, interventions, and implementation strategies. Learners will also be introduced to common types of implementation research questions, including analyzing missed targets, assessing current strategies, and evaluating new strategies.
Implementation Research Methods and Study Designs
- This module introduces common methods and study designs used in implementation research. Learners will examine how implementation research balances internal validity, external validity, feasibility, and relevance to real-world decision-making. The module reviews why implementation studies often use multiple methods, including qualitative approaches to explore implementation problems, strengthen measurement, assess fidelity, and interpret why strategies do or do not work. Learners will also review major design options, including observational, experimental, quasi-experimental, mixed-methods, modeling, and Effectiveness–Implementation Hybrid designs. Through applied activities, learners will practice translating an implementation problem into a research question, specific aims, and a feasible study design.
Implementation Theories, Models, and Frameworks
- This module introduces theories, models, and frameworks commonly used in implementation science. Learners will distinguish among process models, determinant frameworks, and evaluation frameworks, and consider how each can support different implementation research questions. The module emphasizes practical framework selection: choosing a framework based on the implementation problem, stage, context, and purpose of the study. Learners will also practice using a determinant framework, such as CFIR, to organize and prioritize likely barriers and facilitators for assessment, strategy selection, and later evaluation.
Implementation Strategies
- This module focuses on implementation strategies: the actions used to help evidence-based interventions, programs, or policies work in real-world settings. Learners will distinguish implementation strategies from the clinical, public health, or service delivery interventions they are intended to support. The module introduces structured ways to name, specify, and report strategies, including key elements such as actor, action, target, dose, timing, and justification. Learners will also practice using ERIC terminology and determinant-to-strategy logic to build coherent multi-component strategy bundles. The module concludes by considering how strategies can be adapted to local context while preserving their core functions.
Implementation Outcomes and Measures
- This module focuses on how to define and measure implementation success. Learners will distinguish implementation outcomes from clinical effectiveness, service delivery, and health outcomes. The module introduces common implementation outcomes, such as acceptability, adoption, appropriateness, feasibility, fidelity, penetration, cost, and sustainment, and explains how outcome selection should align with the implementation stage, strategy theory of change, and intended use of findings. Learners will also consider pragmatic measurement options, including surveys, administrative data, program records, observation, interviews, and other feasible data sources. The module concludes with basic measurement quality considerations, including reliability, validity, feasibility, relevance, and fit with the implementation context.
Implementation Study Designs
- This module focuses on study designs used to evaluate implementation strategies in real-world settings. Learners will compare randomized, quasi-experimental, pragmatic, and adaptive approaches, and consider how each design balances feasibility, validity, ethics, and usefulness for decision-making. The module introduces common randomized approaches, such as cluster randomized and stepped-wedge designs, as well as non-randomized approaches used when randomization is not feasible. Learners will also use the pragmatic–explanatory continuum, including tools such as PRECIS-2, to assess how closely a trial reflects real-world conditions. The module concludes with optimization and adaptive designs, such as factorial designs and SMARTs, and asks learners to justify study design choices for evaluating an implementation strategy bundle.
Synthesis and Conclusion
- This final module brings together the major concepts covered throughout the course and considers how learners can apply implementation science beyond the course. Learners will revisit the full implementation research pathway: identifying an implementation problem, understanding context and determinants, selecting and specifying implementation strategies, choosing outcomes and measures, and proposing feasible study designs. The module also provides resources for continued learning, including key terms, frameworks, reporting tools, and references that learners can return to as they design, implement, evaluate, or improve real-world programs and services.