Why do patients experience delays or encounter deviations from care protocols? Where do bottlenecks, inefficiencies, or errors arise in complex clinical workflows? How can healthcare organizations better understand and optimize processes that are flexible, adaptive, and context-dependent? Process mining (PM) provides methods to illuminate hidden pathways in these uncharted workflows integrating heterogeneous data—structured, semi-structured, and unstructured—as well as multiple perspectives reflecting differences between roles in care processes and between individual clinicians, to uncover patterns and actionable insights often missed by conventional analytics. This paper reviews the contributions of PM in healthcare, highlighting its ability to interpret flexible processes, connect system-level and human-level perspectives, and support improvements in care delivery and operational efficiency. We also discuss seminal work by Wil van der Aalst and colleagues, situating PM within the evolving field of healthcare analytics and illustrating its methodological foundations.