Healthcare providers spend a significant amount of time reading and synthesizing electronic health records (EHRs), negatively impacting patient outcomes and causing provider burnout. Traditional supervised machine learning approaches using large language models (LLMs) to summarize clinical text have struggled due to hallucinations and lack of relevant training data. Here, we present a novel, simplified solution for the "Discharge Me!" shared task. Our approach mimics human clinical workflow, using pre-trained LLMs to answer specific questions and summarize the answers obtained from discharge summaries and other EHR sections. This method (i) avoids hallucinations through hybrid-RAG/zero-shot contextualized prompting; (ii) requires no extensive training or fine-tuning; and (iii) is adaptable to various clinical tasks.