Processes in organisations such as hospitals, may deviate from intended standard processes, due to unforeseeable events and the complexity of the organisation. For hospitals, the knowledge of actual patient streams for patient populations (e.g., severe or non-severe cases) is important for quality control and improvement. Process discovery from event data in electronic health records can shed light on the patient flows, but their comparison for different populations is cumbersome and time-consuming. In this paper, we present an approach for the automatic comparison of process models extracted from events in electronic health records. Concretely, we propose to compare processes for different patient populations by cross-log conformance checking, and standard graph similarity measures obtained from the directed graph underlying the process model. Results from a case study on breast cancer care show that average fitness and precision of cross-log conformance checks provide good indications of process similarity and therefore can guide the direction of further investigation for process improvement.