Data Driven Insights in Perioperative Workflows

Abstract

Operating room (OR) workflows are characterized by complex material flows, strict time constraints, and high coordination demands. Disruptions in perioperative ma terial preparation, particularly during the picking phase performed by OR nurses, represent an important but underexplored source of inefficiency. This thesis investigated how material-related OR workflow data can be used to ana lyze workflow behavior, identify disruptions and inefficiencies, and generate insights relevant for decision support. The study was exploratory and based on shadow ing data from a Swedish hospital, complemented by semi-structured interviews and, where necessary, synthetic data. The work was conducted in collaboration with Mölnlycke Health Care. The findings suggest that disruptions in the picking process are closely connected to information fragmentation, unclear material availability, reliance on tacit knowl edge, and changes in the surgical schedule. The quantitative analysis illustrated how variables such as picking duration, interruptions, waiting time, information search, perceived complexity, and staff experience could describe workflow varia tion. However, due to the small sample size and data limitations, the results should be interpreted as exploratory and indicative, rather than statistically generalizable. Process mapping, visualization, and process mining demonstrated how material centric workflows can be made more visible and interpretable for different stakehold ers. The thesis contributes a framework for understanding perioperative material workflows and highlights the need for high-quality structured data to support fu ture workflow analysis, stakeholder-adapted visualization, and clinically meaningful decision support.