Care pathways constitute evidence-based “best-practices” to coordinate the activities and roles for diagnosing and treating illnesses. A patient’s actual series of clinical activities, their care trajectory, may differ from these pathways for multiple reasons—differing knowledge and experiences of clinicians, local practices, or multiple patient morbidities. Checking the compliance of care trajectories with clinical pathways not only tests their alignment with evidence-based practices, but may also identify opportunities for improving pathways; such as assessing their effectiveness in practice, and finding non-standardized yet valuable pathways for complex cases. To conduct compliance checking on a meaningful scale, we can use process (and decision) mining to extract care trajectories as process models from clinical data. In turn, when considering care pathways as “reference” process models, the latter can be extracted from guidelines using process extraction techniques. While challenges remain in mining care trajectories, a major barrier involves automated process extraction from clinical text. In this regard, novel Large Language Models (LLMs) frameworks offer promise. This paper outlines a methodology for checking pathway compliance using process mining and process extraction. To assess the feasibility of the latter, we present initial evaluation results using multiple state-of-the-art LLM architectures for Quality-Based Procedures from Ontario (Canada).