Presentation (CTI)

OBJECTIVES: Existing-Use HealthTech guidance compares technologies already used in clinical practice, where user experience may highlight factors influencing technology selection (user preference) that are not captured in existing HTA assessments. We aimed to develop and pilot a transparent, reproducible MCDA-informed method to elicit and present user preferences for consideration in NICE guidance development.
METHODS: An MCDA-based framework was developed iteratively through consultation with NICE staff, commissioned academic groups, MCDA specialists, and industry representatives. Initially, the aim was to use a quantitative additive MCDA model to compare technologies and support value judgements beyond willingness-to-pay thresholds.
Feedback from workshops, external consultation, and 8 pilot existing-use assessments led to a modified approach. Key drivers included moderate participation (10-15 participants) and the need for timely NHS guidance, limiting opportunities for iterative testing and formal validation of criteria and performance matrices. These constraints raised concerns about interpreting quantitative outputs for value-based judgements.
RESULTS: The final process retains SMART ranking and swing weighting methods to structure user preference elicitation through workshops and email-based exercises to identify, rank, and weight criteria, alongside the development of a performance matrix. This performance matrix is then used to assess each of the technologies in the most important UP criteria.
The committee considers this assessment alongside other evidence, but it does not supersede the health economic assessment or affect the willingness-to-pay threshold. The output identifies users’ most important criteria, their relative importance, and each technology’s performance against them, showing how well the evidence reflects what matters to users.
CONCLUSIONS: A modified MCDA-informed approach was developed through stakeholder engagement and piloting. It is feasible within NICE Existing Use HealthTech Guidance and improves transparency and structure in capturing user preferences. Although practical constraints regarding resource and time limited full implementation of formal MCDA methods, the process provides a transparent and reproducible approach.