Evaluating Rare Disease Diagnostic Performance in Symptom Checkers: A Synthetic Vignette Simulation Approach

Symptom Checkers (SCs) provide users with personalized medical information. To prevent performance degradation from algorithm updates, SC developers must evaluate diagnostic performance changes for individual diseases before deployment. However, acquiring sufficient evaluation data for rare diseases is difficult, and manually creating numerous clinical vignettes is costly and impractical. This study proposes and validates a novel Synthetic Vignette Simulation Approach to evaluate diagnostic performance changes for individual rare diseases following SC algorithm updates. We used disease-phenotype annotations from the Human Phenotype Ontology (HPO), a knowledge database for rare diseases, to generate synthetic vignettes. With these, we simulated SC interviews to estimate the impact of algorithm updates on real-world diagnostic performance. The method's effectiveness was evaluated retrospectively by comparing estimated values with actual metric changes using the $R^2$ coefficient. The experiment included eight past SC algorithm updates. For updates on diseases with frequency information in HPO (n=5), the $R^2$ for Recall@8 change was 0.831 ($p$=0.031), and for Precision@8 change, it was 0.78 ($p$=0.047), indicating the method can predict post-deployment performance. In contrast, large prediction errors occurred for diseases without frequency information (n=3), highlighting its importance. Our method enables pre-deployment evaluation of SC algorithm changes for individual rare diseases using a publicly available, expert-created knowledge base. This transparent and low-cost approach allows developers to efficiently improve diagnostic performance for rare diseases, potentially enhancing support for early diagnosis.