Dynamic complexity adaptation: how LLMs enhance medication information literacy
DOI:
https://doi.org/10.47989/ir31iConf64271Keywords:
Medication literacy, Information literacy, LLMs, MedLitRxSEAbstract
Introduction. This study investigates how large language models (LLMs) enhance medication information literacy through dynamic complexity adaptation. Addressing global medication safety challenges exacerbated by low literacy, we evaluate whether LLM-generated content surpasses human community answers in reliability while balancing professionalism and accessibility.
Method. Using multi-source medical Q&A datasets (184,843 entries), we curated 1,229 medication literacy-focused units (4,916 entries) assessed via the standardised MedLitRxSE scale. Double-blind scoring (κ=0.82) and ANOVA compared three LLM complexity variants against baseline answers.
Results. Professionally calibrated LLM content (Variant 5) significantly outperformed human- generated answers across 78.6% of literacy metrics (p<0.001), excelling in risk communication (+30%) and operational knowledge (+17%). A ‘golden interval’ for optimal comprehension was identified at terminology density (15–25%) and dependency distance (3–4 words). Conversely, oversimplified content (Variant 1) reduced dose accuracy and increased ethical risks by 62.3%.
Conclusion(s). LLMs elevate medication literacy through risk-stratified complexity adaptation: high-risk scenarios (e.g., dosing) demand uncompromised precision (Variant 5), while lower- stakes contexts (e.g., storage) tolerate simplification (Variant 1). This framework resolves the readability paradox, proving accessibility requires professional accuracy. Future work must expand into multilingual validation and hybrid generation strategies.
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