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This paper introduces a clinician-facing chatbot designed to support decision-making for Long COVID by integrating expert-curated consensus guidance with real-time data from multiple sources, including PubMed literature and ongoing clinical trials. The system employs a retrieval-augmented workflow that ensures consensus guidance is always prioritized, while additional evidence is retrieved based on user queries. In an exploratory evaluation involving 50 clinician questions, the chatbot achieved comparable performance to OpenEvidence, demonstrating higher scores and lower variability in assessments by language models.
Clinicians can now access a chatbot that seamlessly integrates consensus guidance with the latest research, enhancing decision-making for Long COVID.
Long COVID (LC) poses a challenge for clinical decision support because relevant evidence is distributed across sources with different update cycles, evidentiary roles, and levels of clinical maturity. We present a clinician-facing chatbot that organizes four sources within a retrieval-augmented workflow: expert-curated consensus guidance, current PubMed literature, registered interventional trials, and evidence from living systematic reviews. Consensus guidance is always included to frame responses, while the remaining sources are retrieved in parallel when selected by the user. In an exploratory automated evaluation on 50 clinician-facing questions, our chatbot showed comparable mean ratings to OpenEvidence, with numerically higher scores and lower score variability in an LLM-judged comparison.
