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Schema Enforcement and Structured-Output Stability in Locally Deployed LLMs for Clinical Admission-Note Editing: A
Ya-Lun Yang1, Chia-Jung Chen1, Tin-Kwang Lin2,3
1Department of Nursing, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Dalin 622401, Chiayi, Taiwan.
Healthcare (Basel, Switzerland)
|July 28, 2026
Summary
Schema enforcement significantly improved the reliability of large language models (LLMs) for structured clinical documentation, ensuring parseable and compliant outputs. However, models still exhibited unique documentation behaviors, highlighting the need for proxy-based screening.
Area of Science:
- Artificial Intelligence
- Medical Informatics
Background:
- Structured output generation is crucial for integrating large language models (LLMs) into automated clinical documentation.
- Current evaluations often prioritize clinical quality over output parseability, schema compliance, and stability.
Purpose of the Study:
- To evaluate the structured-output generation capabilities of three open-weight LLMs for clinical documentation tasks.
- To compare the performance of LLMs under free-text JSON prompting versus schema-enforced structured-output conditions.
Main Methods:
- Three LLMs (Llama3-Med42-8B, Meta-Llama-3-8B-Instruct, Mistral-7B-Instruct-v0.3) were tested on 70 de-identified admission notes.
- Evaluations included free-text JSON prompts and schema-enforced outputs, with automated proxy metrics assessing validity and stability.
- A total of 1260 local inferences were performed on consumer-grade hardware.
Main Results:
- Schema enforcement resulted in 100% first-pass valid and schema-compliant outputs for all models.
- Free-text prompting showed variable reliability, with Mistral-7B-Instruct-v0.3 exhibiting the weakest structural reliability.
- Embedding system instructions within user messages increased invalid outputs compared to separate roles.
Conclusions:
- Schema enforcement effectively eliminates parsing and schema failures in LLM-generated clinical documentation.
- Despite structural improvements, LLMs demonstrate distinct documentation behaviors, necessitating further evaluation.
- Proxy-based screening is valuable for identifying unstable LLM configurations prior to clinical review.
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