OP02 The Use Of Discrete Choice Experiments For Measuring Patient Preferences In Health Technology Assessment

Introduction Understanding patient preferences and the demand for healthcare interventions and technology is critical for health technology assessment (HTA). New health technologies have potential for savings and increased efficiency but even the most cost-effective and efficacious interventions can fail if patient preferences are not properly accounted for. Patient preferences in HTA are primarily limited to representation in appraisal committees; however, more robust methods are available and should be incorporated into the assessment of interventions. Methods Using data from three discrete choice experiments (DCEs), we reflect on the importance of patient preferences in the design of healthcare interventions. We draw insights from three studies which investigated preferences relating to HIV self-testing amongst long distance truck drivers in Kenya; differentiated antiretroviral therapy services amongst stable HIV patients in Zimbabwe; and tuberculosis preventive therapy for children in Eswatini. Results We highlight three key findings. First, understanding patient preferences is crucial when designing services, and providers sometimes underestimate behavioural barriers and overestimate the extent to which people are motivated simply by health benefits. Optimism is often driven by evidence showing high acceptability, but when preference structures are incorporated in intervention design, there are important insights into how patients plan to utilize services. Second, trade-offs matter in determining which characteristics are perceived to be most important to patients – a key strength of the DCE methodology. Understanding of these trade-offs can help prioritize which characteristics of interventions to target. Finally, disentangling the effect of different characteristics of service delivery models on preferences is important for rethinking how interventions are delivered. If services are designed to better align with preferences, implementers can ensure new interventions have the desired effect on health and economic outcomes. Conclusions These findings highlight the value of behavioural economic approaches for investigating preferences for health interventions and providing insights into the demand for services, which must feed into the HTA analyses. Incorporating DCEs into HTA is inexpensive and provides robust data for improving HTA.