Can healthcare choice be predicted using stated preference data?

Lack of evidence about the external validity of Discrete Choice Experiments (DCEs)-sourced preferences inhibits greater use of DCEs in healthcare decision-making. This study examines the external validity of such preferences, unravels its determinants, and provides evidence of whether healthcare choice is predictable. We focused on influenza vaccination and used a six-step approach: i) literature study, ii) expert interviews, iii) focus groups, iv) survey including a DCE, v) field data, and vi) in-depth interviews with respondents who showed discordance between stated choices and actual healthcare utilization. Respondents without missing values in the survey and the actual healthcare utilization (377/499 = 76%) were included in the analyses. Random-utility-maximization and random-regret-minimization models were used to analyze the DCE data, whereas the in-depth interviews combined five scientific theories to explain discordance. When models took into account both scale and preference heterogeneity, real-world choices to opt for influenza vaccination were correctly predicted by DCE at an aggregate level, and 91% of choices were correctly predicted at an individual level. There was 13% (49/377) discordance between stated choices and actual healthcare utilization. In-depth interviews showed that several dimensions played a role in clarifying this discordance: attitude, social support, action of planning, barriers, and intention. Evidence was found that our DCE yields accurate actual healthcare choice predictions if at least scale and preference heterogeneity are taken into account. Analysis of discordant subjects showed that we can even do better. The DCE measures an important part of preferences by focusing on attribute tradeoffs that people make in their decision to participate in a healthcare intervention. Inhibitors may be among these attributes, but it is more likely that inhibitors have to do with exogenous factors like goals, religion, and social norms. Con-ducting upfront work on constraints/inhibitors of the focal behavior, not just what promotes the behavior, might further improve predictive ability.

Patients' and urologists' preferences for prostate cancer treatment: a discrete choice experiment.

BACKGROUND: Patients' preferences are important for shared decision making. Therefore, we investigated patients' and urologists' preferences for treatment alternatives for early prostate cancer (PC). METHODS: A discrete choice experiment was conducted among 150 patients who were waiting for their biopsy results, and 150 urologists. Regression analysis was used to determine patients' and urologists' stated preferences using scenarios based on PC treatment modality (radiotherapy, surgery, and active surveillance (AS)), and risks of urinary incontinence and erectile dysfunction. RESULTS: The response rate was 110 out of 150 (73%) for patients and 50 out of 150 (33%) for urologists. Risk of urinary incontinence was an important determinant of both patients' and urologists' stated preferences for PC treatment (P<0.05). Treatment modality also influenced patients' stated preferences (P<0.05), whereas the risk of erectile dysfunction due to radiotherapy was mainly important to urologists (P<0.05). Both patients and urologists preferred AS to radical treatment, with the exception of patients with anxious/depressed feelings who preferred radical treatment to AS. CONCLUSION: Although patients and urologists generally may prefer similar treatments for PC, they showed different trade-offs between various specific treatment aspects. This implies that urologists need to be aware of potential differences compared with the patient's perspective on treatment decisions in shared decision making on PC treatment.