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Purpose: The purpose of this study is twofold: (1) to derive a workflow consensus from multiple clinical activity logs and (2) to detect workflow outliers automatically and without prior knowledge from experts.Methods: Workflow mining is used in this paper to derive consensus workflow from multiple surgical activity logs using tree-guided multiple sequence alignment.To detect outliers,a global pair-wise sequence alignment (Needleman–Wunsch) algorithm is used.The proposed method is validated for Laparoscopic Cholecystectomy (LAPCHOL).Results: An activity log is directly derived for each LAPCHOL surgery from laparoscopic video using an already developed instrument tracking tool.We showed that a generic consensus can be derived from surgical activity logs using multi-alignment.In total 26 surgery logs are used to derive the consensus for laparoscopic cholecystectomy.The derived consensus conforms to the main steps of laparoscopic cholecystectomy as described in best practices.Using global pair-wise alignment,we showed that outliers can be detected from surgeries using the consensus and the surgical activity log.Conclusion: Alignment techniques can be used to derive consensus and to detect outliers from clinical activity logs.Detecting outliers particularly in surgery is a main step to automatically mine and analyse the underlying cause of these outliers and improve surgical practices.