How Can We Move From Generating Robust Patient Preference Information to Producing Decision-Ready Outputs?

PURPOSE:

This workshop will discuss challenges and solutions to transforming patient preference information into decision-ready outputs.

DESCRIPTION:

Background: Two decades of advancements in stated preference methods have improved the reliability of patient preference information. To ensure these improvements are accessible to practitioners, they have been reflected in various ISPOR task forces, workshops, and issue panels. While the scientific community has since moved on from methodological discussions to reflecting on the use of preference information in decision making, the development of tools and approaches to generate decision-ready outputs has just started to receive attention. As a field, we therefore understand how to generate reliable data and how that data could be used, but still lack methods for generating fit-for-purpose outputs. This workshop will discuss and demonstrate various methods that can transform patient preference information into outputs that align with the needs for making a wide range of health care decisions.

Leader: Sebastian Heidenreich will build on recommendations from a recent ISPOR task force to discuss preference-based trial simulations to overcome current limitations in benefit-risk assessments. The discussion will specifically outline opportunities that result from the inclusion of preference elicitation methods in pivotal trials, to help facilitate the interpretation of clinical data. Paul Schneider will demonstrate the suitability of novel preference elicitation instruments to develop individual specific decision tools that can be used in advisory board settings and shared decision-making contexts. For this purpose, a live preference elicitation will be conducted with individual specific treatment recommendations based on unique preferences. Divya Mohan will draw on tools from economic evaluations by demonstrating how an individual level Markov Model can be used to quantify the costs and benefits of preference-based decision making to the health care system. The results will be contrasted with QALY-based models to interpret findings within an established decision-framework.