Starting with the End in Mind: Recommendations to Optimize Implementation of a Novel TBI Classification from the 2024...

The Knowledge to Practice Working Group (K2P WG) was one of six expert groups convened in early 2023 to plan the 2024 National Institute of Neurological Disorders and Stroke Traumatic brain injury (TBI) Classification and Nomenclature Workshop. Recognizing that implementation of revised classification systems is essential to achieve intended impact, the K2P WG's key aims were to foster shared understanding of knowledge translation (KT), build capacity for implementation of a revised TBI classification system, identify and prioritize KT actions, implementation steps and audiences; and make recommendations to advance implementation. The cornerstone of this work was a focused survey to identify "who needs to do what differently," while prioritizing potential implementation actions. Survey findings, dialogue with other working groups, stakeholder discussions, and public feedback were also utilized to support implementation of the revised Clinical, Biomarker, Imaging-Modifiers and retrospective TBI classification system. Forty researchers across five working groups responded to the survey (Response Rate = 59.7%). Fifty-two unique implementation actions were identified. The top 15 priorities across the five working groups comprised six pertaining to clinical practice (e.g., change Glasgow Coma Scale [GCS] assessment); seven focusing on research (e.g., develop tools for measuring psychological and environmental factors); and one each on lived experience (simplified language for patients and families) and other settings (insurance company support for biomarker testing). Twenty-seven stakeholder groups and 18 target settings were identified as being most impacted by the revised classification system. Key recommendations included: develop guidelines based on systematic reviews, clearly explain the rationale for the change, develop implementation toolkits with input from all stakeholders, and embed the new classification in a learning health system database to facilitate implementation strategies based on audits, feedback, and cost-effectiveness analyses.