Preoperative information needs and preferences of refractive surgery patients: a discrete choice experiment

Background: Individualized preoperative information can enhance patient satisfaction. However, existing studies have methodological limitations, largely adopting a healthcare "supply-side" perspective and lacking a patient-centered approach. Moreover, systematic quantitative assessments of information preferences among refractive surgery patients remain scarce. Settings: Department of Ophthalmology at a tertiary general hospital in Luzhou and a tertiary ophthalmic specialized hospital in Shenzhen, China. Participants: A total of 119 patients in the pilot survey phase, and 567 patients in the formal survey phase. Methods: Attributes and levels were identified through literature review, qualitative interviews, expert panel consultation and importance ranking. An orthogonal design was generated using Ngene for pilot choice sets, with a D-efficient design subsequently optimized for the main survey. Model estimation was performed in Stata 17.0, commencing with a multinomial logit (MNL) model and a random parameters logit (RPL) model to capture unobserved preference heterogeneity. A latent class logit (LCL) model was subsequently estimated to identify preference-based subgroups. Attribute interaction effects were examined to explore potential complementarities and substitutabilities. Finally, Scenario prediction analysis were conducted to predict the uptake probabilities of alternative information packages. Results: < 0.001], indicating complex complementary and substitution patterns, though individual interactions were not significant after correction. The optimal combination was identified as "in-depth information" + "standardized + personalized" + "music" + "illustrated manual + video explanation + WeChat push notification" + "real-time interaction" + "one day before surgery" + "<30 min." Conclusion: Patients value humanized care, efficient communication, and respect for their time. Clinical education should shift from a "one-size-fits-all" approach to individualized optimization, enhancing overall utility through optimized combinations. The ideal service model constructed in this study provides evidence-based guidance for optimizing preoperative education processes.