Workflow mining and outlier detection from clinical activity logs

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.

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).

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.

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.