An LLM's Medical Testing Recommendations in a Nigerian Clinic: Potential and Limits of Prompt Engineering for Clinical Decision Support

Abstract

We explore prompt designs for a lightweight integration of a large language model (LLM) into clinical decision support in primary care in Nigeria. The LLM integration is designed to give immediate, actionable “second opinions” to frontline healthworkers on their patient interaction notes. The assessment of a physician serves as a benchmark for the quality of the LLM feedback. A particular challenge was to counter the LLM's tendency to over-recommend laboratory testing, which is more in line with medical practice in high-income countries. We evaluate the ability of a range of prompt engineering approaches to better align the LLM's medical test recommendations with locally appropriate standards of clinical care.

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