Clinical guidelines serve as normative process models for healthcare organizations but are often presented in unstructured, textual formats. This lack of formalization hinders the use of traditional conformance checking algorithms, which require structured, machine-readable process descriptions. In this study, we address this challenge by: (i) employing a Large Language Model (LLM) to extract normative rules from textual guidelines; (ii) assessing and quantifying the conformance of patient event logs to these rules; and (iii) using this framework to evaluate the quality of process models generated by various process discovery algorithms, based on their conformance to the extracted rules. In the paper, we present the approach, which represents the first step of a larger and more ambitious project, and its first results in the domain of stroke care.