Multimorbidity is a global public health challenge, where an individual has two or more chronic conditions, making it difficult to treat and manage illnesses. Understanding the disease trajectories of multimorbidity is crucial for providing patient-centred care. Previous research has primarily employed regression-based approaches, which don’t consider the specific diseases involved and the order in which they occur. Process mining was recently proposed to address this gap, showing promising results in modelling disease trajectories across the entire spectrum of diseases. However, that study involved admissions to a single hospital, and hence the size of the dataset was much smaller than what is typically used in population-level studies on multimorbidity. In this paper, we present a case study where process mining techniques are applied to a much larger dataset of patients in Scotland. We present the disease trajectories discovered for the entire population, as well as stratified by sex. We also describe temporal patterns of disease trajectories, including trajectories with rapid progression. Finally, we discuss the experience of employing process mining within a trusted research environment, and we reflect on challenges that we faced when mining disease trajectories based on a large and complex dataset. Our main contribution involves providing additional evidence around the feasibility of disease trajectory modelling through process mining techniques, in particular when a much larger health dataset is involved.