Measuring the sensitivity of LLM-based structured extraction to prompt, model, and schema choices in clinical discharge summaries
Large language models are increasingly used for structured extraction from clinical free-text notes, but the sensitivity of their output to upstream configuration choices is less understood than their accuracy on fixed benchmarks. This work measures that sensitivity without human-annotated ground truth, by holding the extraction task fixed and varying one choice at a time. The fixed schema comprises 17 clinical documentation flags on a three-way yes/no/not_documented value set and a 47-tag vocabulary for the primary admission reason. Three prompt variants expressing this schema were each run at two model sizes on MIMIC-IV v3.1 discharge summaries. Cross-prompt agreement was measured by Co...
| Authors | Martin Murin |
|---|---|
| Primary category | cs.CL |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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