Remote clinical care has significantly increased the workload for healthcare professionals managing digital inquiries. While automated systems aim to alleviate this burden, consumer health questions present unique challenges due to their linguistic complexity and the need for proactive clinical guidance, which traditional question-answering models often overlook. We introduce the medical Response Content Units (RCU) schema, a framework that facilitates automatic analysis to identify question-answer completeness and critical answer subparts, which can then be used as tools for supporting clinician response or for automatic metric evaluation. Our analysis using this schema reveals a 16.4% gap in response completeness in professional replies and demonstrates that essential medical directives are provided 2.4 to 12.1 times as frequently as direct answers. We provide baseline results and publicly release our annotations and source code to offer an evaluation framework that is more closely aligned with real-world clinical requirements.

In the following paper, we present our team’s approach to subtask 1.1 of the BioLaySumm 2025 shared task, which entails the automated generation of lay summaries from biomedical articles. To this end, we experiment with a variety of methods for text preprocessing, extractive summarization, model fine-tuning, and abstractive summarization. Our final results are generated on a fine-tuned Llama 3.1 Instruct (8B) model, notably achieving top scores on two out of four relevance metrics, as well as the highest overall ranking among this year’s participating teams on the plain lay summarization subtask.