Patient-Preference Diagnostics: Adapting Stated-Preference Methods to Inform Effective Shared Decision Making

Background While clinical practice guidelines underscore the need to incorporate patient preferences in clinical decision making, incorporating meaningful assessment of patient preferences in clinical encounters is challenging. Structured approaches that combine quantitative patient preferences and clinical evidence could facilitate effective patient-provider communication and more patient-centric health care decisions. Adaptive conjoint or stated-preference approaches can identify individual preference parameters, but they can require a relatively large number of choice questions or simplifying assumptions about the error with which preferences are elicited. Method We propose an approach to efficiently diagnose preferences of patients for outcomes of treatment alternatives by leveraging prior information on patient preferences to generate adaptive choice questions to identify a patient's proximity to known preference phenotypes. This information can be used for measuring sensitivity and specificity, much like any other diagnostic procedure. We simulated responses with varying levels of choice errors for hypothetical patients with specific preference profiles to measure sensitivity and specificity of a 2-question preference diagnostic. Results We identified 4 classes representing distinct preference profiles for patients who participated in a previous first-time anterior shoulder dislocation (FTASD) survey. Posterior probabilities of class membership at the end of a 2-question sequence ranged from 87% to 89%. We found that specificity and sensitivity of the 2-question sequences were robust to respondent errors. The questions appeared to have better specificity than sensitivity. Conclusions Our results suggest that this approach could help diagnose patient preferences for treatments for a condition such as FTASD with acceptable precision using as few as 2 choice questions. Such preference-diagnostic tools could be used to improve and document alignment of treatment choices and patient preferences.

Automated summary

This study proposes an efficient approach to diagnose patient preferences for treatment outcomes by utilizing prior preference information and generating adaptive choice questions. The results suggest that this approach can accurately measure patient preferences with as few as 2 choice questions, improving patient-provider communication and facilitating patient-centric health care decisions.