Barriers and facilitators to implementing the living guideline development framework in oncology: a mixed methods study

OBJECTIVE: To explore stakeholder experiences with implementing the living guideline (LG) development framework in oncology, and to identify barriers, facilitators and solutions to support its uptake and sustainability. DESIGN: An exploratory sequential mixed methods design was used, beginning with qualitative semi-structured interviews with guideline development stakeholders, analysed thematically; and followed by a cross-sectional survey to quantitatively rate the importance of factors identified in phase one. SETTING: National and international oncology guideline development programmes using the LG development framework. PARTICIPANTS: Stakeholders involved in LG development, including expert panel co-chairs, panel members, patient representatives, methodologists and administrative staff. RESULTS: Nine stakeholders participated in qualitative interviews, and 45 completed the survey. Most participants were male (5/9:56% qualitative; 26/45:58% quantitative) and based in the US (7/9:78% and 29/45:64%, respectively). Overall, the results from both the qualitative and quantitative strand revealed seven themes (34 subthemes) as barriers and six themes (21 subthemes) as facilitators. Additionally, 9 themes were proposed as solutions. The most frequently reported barriers included evidence timeliness, interpretation and publication delays. Prominent facilitators included effective management, resource optimisation and panel engagement. Participants strongly endorsed investment in artificial intelligence enhanced tools to improve the speed and efficiency of evidence acquisition and review. CONCLUSION: While the LG framework provides strong methodological guidance, its practical application presents notable challenges, particularly in resource demands and implementation logistics. Successful adoption requires adequate infrastructure, expertise and oversight. These findings highlight critical considerations for developers aiming to implement sustainable LG models in oncology and beyond.