The use of large language models (LLMs) in healthcare poses challenges related to data security, accuracy, bias, and usability, which can hinder their effectiveness in enhancing patients ability to locate trusted health information. We conducted a pilot study of a local LLM named “SAM,” built upon the Llama 7B architecture. Participants engaged with SAM by submitting health-related questions across five health domains. Following their interactions, participants completed the System Usability Scale (SUS) to evaluate the usability of the model. Of the ten participants, eight (80%) were female, and two (20%) were male. The highest-rated theme was ease of learning, with participants strongly agreeing that most people would quickly learn to use the chatbot (Mean = 4.7, SD = 0.46). Developing a local LLM for patient health information must tackle healthcare barriers. By enhancing data security, personalizing responses, and increasing user familiarity, SAM can improve patient engagement and outcomes.