OBJECTIVES: Decision-making processes in healthcare can involve multiple alternatives that are characterized by different attributes without clear superiority. Stated preference methods aid guiding multi-criteria decision-making processes. While existing methodology allows eliciting aggregated preferences at population-level to overcome limited ascertainable information of one individual patient, theoretic challenges hamper estimating individual preference coefficients robustly. This study’s objective is to identify approaches allowing to assess individual preferences using Discrete Choice Experiments (DCE) and Best-Worst Scaling (BWS).

METHODS: A systematic literature search in PubMed, Google Scholar, and EconBiz has been conducted between April and May 2017. English and German search terms were used to identify literature on individual preference assessment strategies using DCE or BWS methodology. Literature sources were analyzed and synthesized using thematic synthesis in combination with meta-synthesis methodology to integrate findings from diverse study types.

RESULTS: A total of 82 sources have been included in this review; 24 of these sources reported applications of individual preference assessment. Data synthesis revealed three main approaches to elicit individual preferences using DCE and BWS: a. adjusting data generation (e.g., adaptive choice-based conjoint analysis); b. changes in data analysis (e.g., Bayesian analyses); and c. modifying data interpretation (e.g., data expansion, latent class analyses). However, features of evaluated individual preference elicitation methods show considerable overlap across approaches. Studies applying individual preference assessment were assigned to the derived approaches. Eight studies used approach a. data generation; 15 studies utilized approach b. data analysis; five studies employed approach c. data interpretation. One in six applications combined features of more than one approach.

CONCLUSIONS: DCE and BWS offer three main strategies to assess individual-level preference information that are used separately and in combination. Future research is needed to differentiate strengths and weaknesses, and potential best practice guidelines for individual-level preference assessment to enable personalized healthcare based on individual patient preferences.