Identification of knowledge translation theories, models or frameworks suitable for health technology reassessment: a survey of international experts

Abstract

ObjectiveHealth technology reassessment (HTR) is a field focused on managing a technology throughout its life cycle for optimal use. The process results in one of four possible recommendations: increase use, decrease use, no change or complete withdrawal of the technology. However, implementation of these recommendations has been challenging. This paper explores knowledge translation (KT) theories, models and frameworks (TMFs) and their suitability for implementation of HTR recommendations.DesignCross-sectional survey.ParticipantsPurposeful sampling of international KT and HTR experts was administered between January and March 2019.MethodsSixteen full-spectrum KT TMFs were rated by the experts as ‘yes’, ‘partially yes’ or ‘no’ on six criteria: familiarity, logical consistency/plausibility, degree of specificity, accessibility, ease of use and HTR suitability. Consensus was determined as a rating of ≥70% responding ‘yes’. Descriptive statistics and manifest content analysis were conducted on open-ended comments.ResultsEleven HTR and 11 KT experts from Canada, USA, UK, Australia, Germany, Spain, Italy and Sweden participated. Of the 16 KT TMFs, none received ≥70% rating. When ratings of ‘yes’ and ‘partially yes’ were combined, the Consolidated Framework for Implementation Research was considered the most suitable KT TMF by both KT and HTR experts (86%). One additional KT TMF was selected by KT experts: Knowledge to Action framework. HTR experts selected two additional KT TMFs: Co-KT framework and Plan-Do-Study-Act cycle. Experts identified three key characteristics of a KT TMF that may be important to consider: practicality, guidance on implementation and KT TMF adaptability.ConclusionsDespite not reaching an overall ≥70% rating on any of the KT TMFs, experts identified four KT TMFs suitable for HTR. Users may apply these KT TMFs in the implementation of HTR recommendations. In addition, KT TMF characteristics relevant to the field of HTR need to be explored further.