Understanding misimplementation in US state health departments: An agent-based model

Introduction: The research goal of this study is to explore why misimplementation occurs in public health agencies and how it can be reduced. Misimplementation is ending effective activities pre-maturely or continuing ineffective ones, which contributes to wasted resources and suboptimal health outcomes.Methods: The study team created an agent-based model that represents how information flow, fil-tered through organizational structure, capacity, culture, and leadership priorities, shapes continua-tion decisions. This agent-based model used survey data and interviews with state health department personnel across the U.S. between 2014 and 2020; model design and analyses were con-ducted with substantial input from stakeholders between 2019 and 2021. The model was used experimentally to identify potential approaches for reducing misimplementation. Results: Simulations showed that increasing either organizational evidence-based decision-making capacity or information sharing could reduce misimplementation. Shifting leadership priorities to emphasize effectiveness resulted in the largest reduction, whereas organizational restructuring did not reduce misimplementation. Conclusions: The model identifies for the first time a specific set of factors and dynamic pathways most likely driving misimplementation and suggests a number of actionable strategies for reducing it. Priorities for training the public health workforce include evidence-based decision making and effective communication. Organizations will also benefit from an intentional shift in leadership decision-making processes. On the basis of this initial, successful application of agent-based model to misimplementation, this work provides a framework for further analyses.