The ART of evidence-based care: proof of concept of a novel strategy to accelerate research translation in health organisations

Introduction: This study evaluated a novel strategy to improve accessibility of implementation science to clinicians in a large health network. The strategy, called Accelerating Research Translation (ART), was based on an adaptation of the A3 problem-solving method developed for use in healthcare quality improvement. Method: An observational pre-post design was used to conduct a ‘proof-of-concept’ trial of the ART strategy. Clinicians who identified a gap between a clinical practice guideline and usual care were recruited as Project Leads to conduct research translation projects over a 6-month period, supported by a training programme involving a workshop series and mentor. The proportion of patients receiving guideline-consistent care for each project was measured pre- and post-intervention, and results across projects were combined in a planned pooled meta-analysis. Secondary outcomes included changes in confidence in research translation for Project Leads and the degree of evidence of implementation, mapped against Meyer and Goes' stages of healthcare implementation. Results: Ten projects were completed by 13 Project Leads, involving a diverse range of professional disciplines, clinical specialties, and healthcare settings. Pooled analysis of data from 768 patients suggests the ART programme increased the odds of receiving evidence-based care (logOR 2.52, 95% CI: 0.93–4.11, p < 0.01). Effects were stronger for evidence–practice gaps that could be addressed with changes to local policies and processes than those dependent on changing behaviour or complex systems. Confidence of Project Leads improved, and all reported increases in the level of implementation of evidence. Conclusion: This novel approach empowered clinicians to tackle evidence–practice gaps within a familiar structure already well-accepted and understood by health service providers. This concept provides a promising mechanism to demystify implementation science and address local evidence–practice gaps at scale.