Quantifying public preferences for healthcare priorities in Taiwan through an integrated citizens jury and discrete choice experiment.

Priority setting is a critical process for national healthcare systems that need to allocate limited resources across unlimited healthcare demands. In recent decades, health policymakers have identified the need to combine technical dimensions of priority setting with political dimensions relating to community values. A range of methods for engaging the public in priority setting has been developed, yet there is no consensus around the most effective methodology. A 2014 paper proposed the integration of two methods currently used for soliciting public preferences around health care services: i) an individual survey instrument, Discrete Choice Experiments (DCEs) and ii) Citizen Juries (CJs), a group-based model that incorporates education and deliberative dialogue. This pilot study is among the first to empirically test this integrated method to assess its value across two domains: does the CJ process alter participant preferences and are the consensus values of the CJ captured by the individualistic DCE? The two-part, mixed methods study was administered in Taipei, Taiwan in August of 2016. Twenty-seven participants completed a DCE as a baseline pre-test, ranking a set of attributes in terms of importance for future resource allocation under Taiwan's National Health Insurance System. Twenty of the participants next took part in the integrated CJ-DCE method, which consisted of education and facilitated dialogue through a CJ, followed by retaking the DCE survey. Participant preferences changed after undergoing the CJ process and these new, group-based preferences were reflected in the second DCE, meaning participants did not revert to their original individualistic preferences. The results of this study demonstrate that the integrated CJ-DCE method adds value in allowing an ethically communitarian set of values to be developed and captured via an individualistic methodology. Further testing is needed to investigate the reliability of our findings and how it may be implemented to maximize public acceptance.