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Clinical natural language processing requires methods that can addressdomain-specific challenges, such as complex medical terminology and clinicalcontexts. Recently, large language models (LLMs) have shown promise in thisdomain. Yet, their direct deployment can lead to privacy issues and areconstrained by resources. To address this challenge, we delve into syntheticclinical text generation using LLMs for clinical NLP tasks. We propose aninnovative, resource-efficient approach, ClinGen, which infuses knowledge intothe process. Our model involves clinical knowledge extraction andcontext-informed LLM prompting. Both clinical topics and writing styles aredrawn from external domain-specific knowledge graphs and LLMs to guide datageneration. Our extensive empirical study across 7 clinical NLP tasks and 16datasets reveals that ClinGen consistently enhances performance across varioustasks, effectively aligning the distribution of real datasets and significantlyenriching the diversity of generated training instances. We will publish ourcode and all the generated data in \url{https://github.com/ritaranx/ClinGen}.

Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models