Enhancing implementation science by applying best principles of systems science. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Northridge, Mary; Metcalf, Sara
• Is Part Of: Health research policy and systems. Volume 14:Issue 1(2016)
• Page Start: 1
• Page End: 8
• Publication Date: 2016-12
• Subjects: Best principles -- Complexity -- Context -- Implementation science -- Modelling -- Health equity -- Oral health -- Primary care -- Screening at chairside -- Systems science; Public health -- Research -- Developing countries -- Periodicals; Health planning -- Developing countries -- Periodicals; Medical policy -- Developing countries -- Periodicals; 362.107201724
• DOI: 10.1186/s12961-016-0146-8
• Languages: English
• ISSNs: 1478-4505

Abstract

Abstract Background Implementation science holds promise for better ensuring that research is translated into evidence-based policy and practice, but interventions often fail or even worsen the problems they are intended to solve due to a lack of understanding of real world structures and dynamic complexity. While systems science alone cannot possibly solve the major challenges in public health, systems-based approaches may contribute to changing the language and methods for conceptualising and acting within complex systems. The overarching goal of this paper is to improve the modelling used in dissemination and implementation research by applying best principles of systems science. Discussion Best principles, as distinct from the more customary term 'best practices', are used to underscore the need to extract the core issues from the context in which they are embedded in order to better ensure that they are transferable across settings. Toward meaningfully grappling with the complex and challenging problems faced in adopting and integrating evidence-based health interventions and changing practice patterns within specific settings, we propose and illustrate four best principles derived from our systems science experience: (1) model the problem, not the system; (2) pay attention to what is important, not just what is quantifiable; (3) leverage the utility of models as boundary objects; and (4) adopt a portfolio approach to model building. To improve our mental models of the real world, system scientists have created methodologies such as system dynamics, agent-based modelling, geographic information science and social network simulation. To understand dynamic complexity, we need the ability to simulate. Otherwise, our understanding will be limited. The practice of dynamic systems modelling, as discussed herein, is the art and science of linking system structure to behaviour for the purpose of changing structure to improve behaviour. A useful computer model creates a knowledge repository and a virtual library for internally consistent exploration of alternative assumptions. Conclusion Among the benefits of systems modelling are iterative practice, participatory potential and possibility thinking. We trust that the best principles proposed here will resonate with implementation scientists; applying them to the modelling process may abet the translation of research into effective policy and practice.