Investigating Uncertainty in Postoperative Bleeding Management: Design Principles for Decision Support

Decision-making under uncertainty is a difficult and unavoidable challenge in clinical contexts. Technologies such as probabilistic programming languages (PPLs) allow their users to explicitly model and reason with uncertainty. By taking a user-centric approach to the deployment of these technologies, we believe there is an opportunity to involve clinicians in the modelling process. In this paper, we present a field study of decisions taken to manage postoperative bleeding. From analysis of the findings, we outline three central themes that emerge and discuss implications for design, developing a set of evaluative design principles to assess a PPL-based tool in this context. These include visualising zones of optimal intervention, surfacing relative risk trade-offs between teams, and accessing specialist views within a holistic picture. These findings provide a structure for critically exploring PPL-based tools to support clinical reasoning under uncertainty.