ASTRID - An Automated and Scalable TRI a D for the Evaluation of RAG -based Clinical Question Answering Systems (2025)

Abstract: Large Language Models (LLMs) have shown impressive potential in clinical question answering (QA), with Retrieval Augmented Generation (RAG) emerging as a leading approach for ensuring the factual accuracy of model responses. However, current automated RAG metrics perform poorly in clinical and conversational use cases. Using clinical human evaluations of responses is expensive, unscalable, and not conducive to the continuous iterative development of RAG systems. To address these challenges, we introduce ASTRID - an Automated and Scalable TRIaD for evaluating clinical QA systems leveraging RAG - consisting of three metrics: Context Relevance (CR), Refusal Accuracy (RA), and Conversational Faithfulness (CF). Our novel evaluation metric, CF, is designed to better capture the faithfulness of a model’s response to the knowledge base without penalising conversational elements. Additionally, our metric RA captures the refusal to address questions outside of the system’s scope of practice. To validate our triad, we curate a dataset of over 200 real-world patient questions posed to an LLM-based QA agent during surgical follow-up for cataract surgery - the highest volume operation in the world - augmented with clinician-selected questions for emergency, and clinical and non-clinical out-of-domain scenarios. We demonstrate that CF predicts human ratings of faithfulness more accurately than existing definitions in conversational settings. Furthermore, using eight different LLMs, we demonstrate that the three metrics can closely agree with human evaluations, highlighting the potential of these metrics for use in LLM-driven automated evaluation pipelines. Finally, we show that evaluation using our triad of CF, RA, and CR exhibits alignment with clinician assessment for inappropriate, harmful, or unhelpful responses. We also publish the prompts and datasets for these experiments, providing valuable resources for further research and development.

## introduction the healthcare industry is increasingly adopting automation to meet rising demands on resources @xcite . llms, due to their capabilities, have become increasingly popular in supportive clinical applications such as note-taking and summarization @xcite . a crucial aspect of patient care is the ability to ask questions and receive answers, which has been enhanced by advancements in qa systems powered by llms. however, the issue of hallucination remains a significant barrier to using llms for clinical qa systems @xcite . rag is a technique developed to address hallucination and ensure context appropriateness @xcite . despite these advancements, rag systems lack sufficient evaluation metrics and frameworks, making it difficult to quantitatively establish their safety and identify system deficiencies. figure 1 illustrates the limitations of current clinical evaluation approaches and how automated methods address these challenges. this work examines evaluation limitations and applies safety engineering to identify hazard cases in clinical qa @xcite . we develop a robust, scalable framework of metrics to systematically demonstrate how developers can mitigate potential hazards in llmbased qa systems for clinical use. using real patient questions from clinical trials on cataract post-operative recovery, we illustrate how these metrics can be interpreted in a clinical context. we validate our metrics by proving they model human ratings better than previous metrics and effectively predict clinical harm, usefulness, and inappropriateness as labeled by specialist doctors. our aim is to establish a foundation for developing and assessing llm-powered clinical qa systems and encourage further research in this area. ## method we conduct several experiments using datasets sourced from real clinicians and open-source datasets to support the following claims: ## conclusion we present astrid, an automated and scalable triad for evaluating clinical qa systems leveraging rag. astrid comprises three metrics -con-versational faithfulness, refusal accuracy, and context relevance -designed to address the limitations of existing evaluation frameworks in clinical settings. our experiments demonstrate that cf aligns more closely with human judgments of faithfulness compared to previous definitions, and our triad of metrics is the first to correlate system performance measures with clinician assessments of harmfulness, helpfulness, and inappropriateness with high accuracy. we also highlight the potential for these metrics to be automatable using current llms, making them suitable for iterative development and the continuous evaluation of clinical qa systems. by publishing our datasets and prompts, we aim to provide valuable resources for further research and development in the field. future work should expand on end-to-end conversational evaluations and incorporate usability metrics to ensure a comprehensive assessment of clinical qa systems.