Enhancing readability of USFDA patient communications through large language models: a proof-of-concept study.
Expert Rev Clin Pharmacol
; 17(8): 731-741, 2024 Aug.
Article
en En
| MEDLINE
| ID: mdl-38823007
ABSTRACT
BACKGROUND:
The US Food and Drug Administration (USFDA) communicates new drug safety concerns through drug safety communications (DSCs) and medication guides (MGs), which often challenge patients with average reading abilities due to their complexity. This study assesses whether large language models (LLMs) can enhance the readability of these materials.METHODS:
We analyzed the latest DSCs and MGs, using ChatGPT 4.0© and Gemini© to simplify them to a sixth-grade reading level. Outputs were evaluated for readability, technical accuracy, and content inclusiveness.RESULTS:
Original materials were difficult to read (DSCs grade level 13, MGs 22). LLMs significantly improved readability, reducing the grade levels to more accessible readings (Single prompt - DSCs ChatGPT 4.0© 10.1, Gemini© 8; MGs ChatGPT 4.0© 7.1, Gemini© 6.5. Multiple prompts - DSCs ChatGPT 4.0© 10.3, Gemini© 7.5; MGs ChatGPT 4.0© 8, Gemini© 6.8). LLM outputs retained technical accuracy and key messages.CONCLUSION:
LLMs can significantly simplify complex health-related information, making it more accessible to patients. Future research should extend these findings to other languages and patient groups in real-world settings.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
United States Food and Drug Administration
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Comunicación
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Comprensión
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Lenguaje
Límite:
Humans
País/Región como asunto:
America do norte
Idioma:
En
Revista:
Expert Rev Clin Pharmacol
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Reino Unido