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

Area of Science:

• Health Services Research
• Decision Science
• Patient-Reported Outcomes

Background:

• Incorporating patient preferences into clinical decisions is crucial but challenging in practice.
• Existing methods for assessing patient preferences can be time-consuming or rely on simplifying assumptions.
• There is a need for efficient and reliable tools to diagnose individual patient preferences.

Purpose of the Study:

• To develop and evaluate a novel, efficient approach for diagnosing patient preferences for treatment outcomes.
• To leverage prior population-level preference data to create adaptive choice questions.
• To assess the accuracy (sensitivity and specificity) of a brief preference diagnostic tool.

Main Methods:

• Proposed an adaptive approach using prior preference information to generate choice questions.
• Simulated patient responses with varying error levels to test a 2-question preference diagnostic.
• Identified distinct patient preference profiles for first-time anterior shoulder dislocation (FTASD).

Main Results:

• Successfully identified 4 distinct preference profiles for FTASD patients.
• Achieved high posterior probabilities (87-89%) for class membership after only 2 questions.
• Demonstrated that the 2-question diagnostic was robust to respondent errors, with better specificity than sensitivity.

Conclusions:

• A 2-question preference diagnostic can accurately assess patient preferences for conditions like FTASD.
• This approach facilitates patient-provider communication and patient-centric healthcare decisions.
• Preference-diagnostic tools can improve the documentation of alignment between treatment choices and patient values.