Online learnings to inform patients on choice elicitation tasks and assess patient preferences.

PREFER is a 5-year, IMI-funded, project that aims to investigate how and when patient preferences regarding the benefit/risk of drugs can be used in the medical product lifecycle. Multiple case studies use different choice elicitation tasks to investigate how these influence the preferences of patients. To support this research, MindBytes developed interactive educational tools to inform patients on these different methods and how to complete the preference surveys.

Benefits vs. risks
As medicine becomes more personalized, patient preferences become more important to give us critical information for developing medical treatments. These preferences can also tell us how much risk patients think is acceptable for a given benefit. The methods discover or ‘elicit’ patients’ preferences are available, however decision makers are not sure how to assess and use them.
Structured approach

To discover these preferences efficiently, we were missing a structured approach. That is why many partners joined forces for the IMI-PREFER project. This project discovered what the key stakeholders think is important, based on literature review, interviews and focus group meetings with patient organisations, physicians, regulatory authorities, health technology assessment bodies, industry experts and academics.

This framework for patient preference studies has been explained in detail in the PREFER recommendations, which have received a positive qualification opinion from the European Medicines Agency (EMA) in 2022.

Methods for drug life cycle

After this thorough investigation, the PREFER methodology team determined which methods were suitable at different decision points in the drug life cycle. The resulting methods were used in clinical case studies. Using the relatable example of a cough syrup, MindBytes created educational tools to inform patients on the following choice elicitation tasks:

·        Discrete Choice Experiment (DCE)

·        Best-Worst Scaling (BWS)

·        Q-methodology (Q)

·        Swing Weighting (SW)