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Large Language Model-Generated Patient Instructions for Prescriptions in Primary Health Care: Preclinical Algorithm
Zilma Silveira Nogueira Reis1, Elisa Tuler Albergaria2, Adriana Silvina Pagano3
1Health Informatics Center, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Journal of Medical Internet Research
|May 26, 2026
Summary
Generative artificial intelligence (AI) can simplify medication instructions, improving patient adherence. Open-source AI models show promise, though human oversight is crucial for safe integration into electronic prescribing.
Area of Science:
- Artificial Intelligence in Healthcare
- Natural Language Processing for Medical Information
Background:
- Generative AI offers potential to simplify medication instructions, enhancing patient health and treatment adherence.
- Improving clarity of medication use instructions is key for effective primary healthcare.
Purpose of the Study:
- Evaluate the performance of large language models (LLMs) in generating medication usage instructions.
- Compare ChatGPT-4.0, Llama3.1-8B, and Llama3.1-8B-RAG for generating prescription-complementary instructions.
Main Methods:
- Randomized, blinded experimental preclinical study involving 62 healthcare professionals.
- LLMs (ChatGPT-4.0, Llama3.1-8B, Llama3.1-8B-RAG) generated instructions based on patient information leaflets.
- Assessed instructions on adequacy, completeness, clarity, language simplification, usefulness, and errors.
Main Results:
- All models produced qualified instructions, with ChatGPT-4.0 scoring highest overall (median 88.4).
- Llama3.1-8B-RAG performed comparably to ChatGPT-4.0 in adequacy, completeness, clarity, and usefulness.
- Error and hallucination frequencies were similar across models; ChatGPT-4.0 excelled in language simplification.
Conclusions:
- Open-source LLMs (like Llama3.1-8B-RAG) show comparable performance to closed-source models (ChatGPT-4.0), except in language simplification.
- LLM-generated instructions have potential but require prescriber validation and governance for safe electronic prescribing integration.
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