As advancements in research and development expand the capabilities of Large Language Models (LLMs), there is a growing focus on their applications within the healthcare sector, driven by the large volume of data generated in healthcare. There are a few medicine-oriented evaluation datasets and benchmarks for assessing the performance of various LLMs in clinical scenarios; however, there is a paucity of information on the real-world usefulness of LLMs in context-specific scenarios in resource-constrained settings. In this work, 5 iterations of a decision support tool for medical emergencies using 5 distinct generalized LLMs were constructed, alongside a combination of Prompt Engineering and Retrieval Augmented Generation techniques. 50 responses were generated from the LLMs. Quantitative and qualitative evaluations of the LLM responses were provided by 13 physicians (general practitioners) with an average of 3 years of practice experience managing medical emergencies in resource-constrained settings in Ghana. Machine evaluations of the LLM responses were also computed and compared with the expert evaluations.
