The increasing volume of patient messages via electronic health record (EHR) portals has contributed significantly to clinician workload. Automating responses to these messages can help alleviate this burden, but it is essential to ensure that the generated responses are grounded in accurate clinical evidence. As part of the ArchEHR-QA 2025 BioNLP ACL shared task, we explore unsupervised methods for generating patient question responses that are both contextually accurate and evidence-backed. We investigate three novel approaches: zero-shot prompting, clustering-based evidence selection, and attention-based evidence attribution, along with a hybrid model that combines clustering and attention. Our methods do not require model fine-tuning and leverage the inherent structure of the input data to identify the most relevant supporting evidence from clinical notes. Our best-performing approach, which integrates clustering and attention, demonstrates a substantial improvement in factuality over baseline zero-shot methods, highlighting the potential of unsupervised strategies for enhancing the clinical utility of large language models in EHR contexts.