Access to timely investigation and treatment is critical for reducing the clinical and financial burden of cancer. But how to objectively assess the delays and bottlenecks associated with increased wait times and what are possible solutions? In this project, implemented at the Centre hospitalier de l’Université de Montréal (CHUM), we will apply process mining techniques to improve the health care trajectory of patients with lung cancer. Event logs documenting cancer investigation and treatment will be extracted from the CHUM data lake, thereby allowing to discover processes. Algorithms will generate graphical representations of hospital processes and workflows producing care paths and their associated statistics. To assess conformance, we will compare the actual care processes to lung cancer clinical pathways and performance targets. We will then propose and implement organizational changes leading to greater accessibility and quality, and better resource utilization. Furthermore, we will analyze clinical trajectories and we will characterize the cost per care and services associated with use of immunotherapy for lung cancer. CHUM’s process mining approach builds on an existing clinico-administrative data lake infrastructure developed for the purpose of facilitating research, promoting innovation, and improving performance and clinical relevance. This approach could potentially be replicated at other Quebec institutions with similar data access infrastructures.