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Impact of Detailed Versus Generic Instructions on Fine-Tuned Language Models for Patient Discharge Instructions

Muneerah Alqahtani1,2, Abdullah Al-Barakati1, Fahd Alotaibi1

  • 1Information System Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

JMIR Formative Research
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Summary

Detailed instructions significantly improve large language models (LLMs) for generating patient discharge instructions. Task-specific fine-tuning enhances LLM performance in creating clear and accurate post-hospital care guidance.

Keywords:
clinical text generationdischarge instructionsinstruction tuninglarge language modelsmedical natural language processingopen-source large language modelspatient safetyprompt engineering

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Medical Informatics

Background:

  • Discharge instructions are crucial for patient care but are labor-intensive to create.
  • Large language models (LLMs) offer a potential solution for automating the generation of discharge instructions.
  • This study evaluates the efficacy of open-source LLMs for this task.

Purpose of the Study:

  • To assess the capability of a Mistral large language model (LLM) in generating reliable, patient-oriented discharge instructions.
  • To compare two distinct instruction-tuning strategies for fine-tuning the LLM: detailed, task-specific instructions versus generic, minimal guidance.
  • To determine the impact of instruction design on the quality of generated discharge instructions.

Main Methods:

  • Employed the Mistral-NeMo-Instruct large language model (LLM).
  • Fine-tuned the LLM using two instruction strategies: detailed, task-specific guidance and generic, minimal guidance.
  • Evaluated generated discharge instructions against 3621 ground-truth references using multiple metrics including BLEU, ROUGE, SentenceTransformer similarity, and BERTScore.

Main Results:

  • The model fine-tuned with detailed, task-specific instructions significantly outperformed the generic instruction model across all automated evaluation metrics.
  • Key metrics showed substantial improvements: BERTScore increased from 78.92% to 87.05%, ROUGE-L improved from 8.59% to 26.52%, BLEU-4 rose from 0.81% to 21.24%, and ROUGE-1 improved from 16.59% to 42.72%.
  • All observed improvements were statistically significant (P<.001), confirming the benefit of detailed instruction design.

Conclusions:

  • Detailed, task-specific instruction strategies are highly effective in enhancing the performance of open-source large language models (LLMs) for generating discharge instructions.
  • Carefully designed instructions during the fine-tuning process are critical for maximizing LLM performance in medical applications.
  • This approach holds significant promise for improving the efficiency and quality of patient discharge communication.