Rugged landscapes: Complexity and implementation science

Abstract

Background: Mis-implementation - defined as failure to successfully implement and continue evidence-based programs - is widespread in public health practice. Yet the causes of this phenomenon are poorly understood. Methods: We develop an agent-based computational model to explore how complexity hinders effective implementation. The model is adapted from the evolutionary biology literature and incorporates three distinct complexities faced in public health practice: dimensionality, ruggedness, and context-specificity. Agents in the model attempt to solve problems using one of three approaches - Plan-Do-Study-Act (PDSA), evidence-based interventions (EBIs), and evidence-based decision-making (EBDM). Results: The model demonstrates that the most effective approach to implementation and quality improvement depends on the underlying nature of the problem. Rugged problems are best approached with a combination of PDSA and EBI. Context-specific problems are best approached with EBDM. Conclusions: The model's results emphasize the importance of adapting one's approach to the characteristics of the problem at hand. Evidence-based decision-making (EBDM), which combines evidence from multiple independent sources with on-the-ground local knowledge, is a particularly potent strategy for implementation and quality improvement.