Translating the Results of Discrete Choice Experiments into p-/e-/m-Health Decision Support Tools

The rapidly growing number of health-related Discrete Choice Experiments (DCEs) has not been matched by studies of their impact on decision or policymaking. However, it is widely assumed that this impact has been very limited, despite the potential relevance of the resulting average preferences to group policy development. The main, but at the moment essentially speculative, explanation offered, focuses on the methodological quality of the DCEs and their reporting. An alternative explanation, equally speculative, lies in the research-practice gap created by the conceptualisation of the DCE as a purely research exercise, not supplemented by any attempt to translate the findings into analytic decision support form. This also applies in the clinical decision context, where there are frequent claims that DCE results can assist in an individual's decision making. In the absence of suggestions as to how group results can analytically facilitate preference-sensitive care (and legally informed consent), we propose a generic add-on for DCEs with 'real' options, attributes, and attribute levels. This takes the form of a multi-criteria analysis-based decision support tool. Exemplars, showing how preference-sensitive individualised opinions can be derived from published DCEs for Heavy Menstrual Bleeding and Prostate Cancer Screening, may be consulted online.