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Processes in healthcare are complex and data-intensive. Process mining uses data recorded during process execution to obtain an understanding of the actual execution of a process. Due to the complexity of healthcare processes, it is useful to consider and analyse the process execution of certain cohorts, such as old and young patients, separately. While such analysis is facilitated by process variant analysis techniques, existing approaches for process variant analysis only consider a comparison based on the control flow and performance perspectives. Given the large amount of event data attributes available in healthcare settings, we propose the first data-based process variant analysis approach. Our approach allows comparing process variants based on differences in event data attributes by building on statistical tests. We applied our approach on the MIMIC-IV real-world data set on hospitalizations in the US, where we demonstrate that the approach is feasible and can actually provide relevant medical insights.