Discrete choice experiments (DCEs) are increasingly used to elicit preferences for health and healthcare. Although many applications assume preferences are homogenous, there is a growing portfolio of methods to understand both explained (because of observed factors) and unexplained (latent) heterogeneity. Nevertheless, the selection of analytical methods can be challenging and little guidance is available. This study aimed to determine the state of practice in accounting for preference heterogeneity in the analysis of health-related DCEs, including the views and experiences of health preference researchers and an overview of the tools that are commonly used to elicit preferences. An online survey was developed and distributed among health preference researchers and nonhealth method experts, and a systematic review of the DCE literature in health was undertaken to explore the analytical methods used and summarize trends. Most respondents (n = 59 of 70, 84%) agreed that accounting for preference heterogeneity provides a richer understanding of the data. Nevertheless, there was disagreement on how to account for heterogeneity; most (n = 60, 85%) stated that more guidance was needed. Notably, the majority (n = 41, 58%) raised concern about the increasing complexity of analytical methods. Of the 342 studies included in the review, half (n = 175, 51%) used a mixed logit with continuous distributions for the parameters, and a third (n = 110, 32%) used a latent class model. Although there is agreement about the importance of accounting for preference heterogeneity, there are noticeable disagreements and concerns about best practices, resulting in a clear need for further analytical guidance. •There is an increasing interest in accounting for preference heterogeneity in discrete choice experiments, matched with a growing portfolio of analytical methods.•Accounting for heterogeneity allows researchers to go beyond understanding the “average” preference and reduces bias in the estimated parameters.•Most current studies estimate mixed logit models with either continuous (eg, normal, lognormal) or discrete (ie, latent classes) parameter distributions. Some studies attempt to separate heterogeneity in scale and preferences by using more complex models, despite both forms of heterogeneity being statistically confounded.•Health preference researchers are using increasingly complex methods to analyze preference data; nevertheless, our survey suggests there is disagreement among experts and applied researchers on the role, capabilities, and suitability of alternative approaches, indicating a need for discourse, alignment, and guidance.