In response to the ArchEHR-QA 2025 shared task, we present an efficient approach to patient question answering using small, pre-trained models that are widely available to the research community. Our method employs multi-prompt ensembling with models such as Gemma and Mistral, generating binary relevance judgments for clinical evidence extracted from electronic health records (EHRs). We use two distinct prompts (A and B) to assess the relevance of paragraphs to a patient’s question and aggregate the model outputs via a majority vote ensemble. The relevant passages are then summarized using a third prompt (C) with Gemma. By leveraging off-the-shelf models and consumer-grade hardware (1x RTX 5090), we demonstrate that it is possible to improve performance without relying on resource-intensive fine-tuning or training. Additionally, we explore the impact of Chain-of-Thought (CoT) prompting and compare the performance of specialized versus general-purpose models, showing that significant improvements can be achieved through effective use of existing models.