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Background:  Waiting lists are often thought to be inevitable in healthcare, but strategies that address patient flow by reducing complexity, combining triage with initial management, and/or actively managing the relationship between supply and demand can work. One such model, Specific Timely Appointments for Triage (STAT), brings these elements together and has been found in multiple trials to reduce waiting times by 30–40%. The next challenge is to translate this knowledge into practice.   Method:  A multi-faceted knowledge translation strategy, including workshops, resources, dissemination of research findings and a community of practice (CoP) was implemented. A mixed methods evaluation of the strategy was conducted based on the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework, drawing on an internal database and a survey of workshop and CoP participants.   Results:  Demonstrating reach, at July 2020 an internal database held details of 342 clinicians and managers from 64 health services who had participated in the workshop program ( n  = 308) and/or elected to join an online CoP ( n  = 227). 40 of 69 (58%) respondents to a survey of this population reported they had adopted the model, with some providing data demonstrating that the STAT model had been efficacious in reducing waiting time. Perceived barriers to implementation included an overwhelming existing waiting list, an imbalance between supply and demand and lack of resources.   Conclusion:  There is high quality evidence from trials that STAT reduces waiting time. Using the RE-AIM framework, this evaluation of a translation strategy demonstrates uptake of evidence to reduce waiting time in health services.

A Multi-Faceted Strategy for Evidence Translation Reduces Healthcare Waiting Time: A Mixed Methods Study Using the RE-AIM Framework