Related Experiment Video
Updated: Apr 19, 2026

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
Published on: February 19, 2021
Advancing systems thinking in implementation science: An epidemiologic perspective
Danielle Giovenco1, Lindsey M Filiatreau2, Justin Knox3
1Hubert Department of Global Health, Rollins School of Public Health, Emory University, 1518 Clifton Rd NE, Atlanta, GA 30322, USA.
None:
The population-level impact of public health interventions depends on their implementation in real-world settings. This is the purview of implementation science. As the field of implementation science advances, there is growing recognition that complex health challenges demand solutions that account for the dynamic systems in which interventions unfold. Epidemiologists, trained to define causal relationships and quantify population-level effects, are uniquely positioned to contribute to this effort. Yet, realizing that potential requires moving beyond traditional epidemiologic methods, which can be reductionist in nature, and embracing tools from systems thinking. This paper illustrates how integrating epidemiologic methods with principles of systems thinking can strengthen implementation science and inform implementation strategies for evidence-based interventions in complex real-world settings.
More Related Videos
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
10:42Design to Implementation Study for Development and Patient Validation of Paper-Based Toehold Switch Diagnostics
Published on: June 17, 2022
Related Concept Videos
Introduction to Epidemiology
Study Designs in Epidemiology
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Steps in Outbreak Investigation
Principles of Disease Surveillance
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Statistical Methods for Analyzing Epidemiological Data