Right Patient, Right Specialist, Right Time: Retrieval Augmented Generation for Specialty Referral Routing

We present an embedding-based retrieval system that automatically directs physician clinical questions to the most relevant specialist-curated question template, which is necessary for the specialist to provide a clinically relevant response. The system utilizes MPNet, a transformer-based model, to generate dense vector representations of both clinical queries and 24 predefined clinical templates. Given a clinical question, the system computes cosine similarity between the query and template embeddings to retrieve the most relevant matches. When validated against real-world, retrospective eConsults across five specialties, the system accurately identified the most relevant template in 87% of cases (success@1) and included it in the top three results 99% of the time (success@3). Automating specialty selection and clinical question referrals reduces the administrative burden on physicians, minimizes care delivery delays, and improves specialist responses by providing proper context.