Medical insurance plays a pivotal role in the healthcare system, where effective supervision relies on the precise execution of complex medical insurance rules. Existing solutions for medical insurance violation detection mainly rely on costly manual labor, limiting efficiency. Although current Large Language Models (LLMs) have demonstrated significant potential in general Natural Language Processing (NLP) tasks, they still struggle to understand these rules, and a corresponding evaluation benchmark is lacking. Hence, we propose T2ER, a benchmark to evaluate the capability of LLMs in converting texts into executable rules in medical insurance. T2ER consists of 2,115 rules and 5,636 synthetic Electronic Medical Record (EMR) test cases for execution verification. Furthermore, we design a dual evaluation framework spanning rule and execution levels to comprehensively assess transformation quality. We conduct extensive experiments on advanced LLMs. The results reveal a significant human-AI performance gap, indicating that this conversion remains a formidable challenge. The dataset is available at https://github.com/YHJ1223/T2ER.