FHIR benchmark tests local LLMs

// 101d agoRESEARCH PAPER

FHIR benchmark tests local LLMs

An independent researcher is seeking arXiv cs.CL endorsement for a draft clinical NLP benchmark comparing five open-weight models run locally with Ollama on medication reconciliation tasks. The study spans 4,000 inference runs over synthetic FHIR patient records and focuses on how serialization choices affect exact-match F1.

// ANALYSIS

The interesting part is not another small local-LLM shootout; it is the claim that healthcare data formatting can move outcomes as much as model selection.

–Testing Phi-3.5-mini, Mistral-7B, BioMistral-7B, Llama-3.1-8B, and Llama-3.3-70B gives a useful spread across general and biomedical open-weight models
–Four FHIR serialization strategies make this more relevant to real clinical NLP pipelines than generic prompt benchmarks
–Synthetic patients lower privacy risk, but they also limit how strongly the results can generalize to messy clinical records
–The post does not disclose the draft, scores, prompts, or code yet, so this is more an endorsement request than a publishable benchmark result

// TAGS

fhir-medication-reconciliation-benchmarkollamallmopen-weightsinferencebenchmarkresearch

DISCOVERED

101d ago

2026-04-22

PUBLISHED

101d ago

2026-04-22

RELEVANCE

6/ 10

AUTHOR

Ecstatic-Union-1314

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FHIR benchmark tests local LLMs