Visual Digital Twins for Arthroplasty: Conceptual Dashboards Integrating Patient Clusters and Clinical Pathways

Digital twins have increasingly been proposed as a means of supporting personalized clinical decision-making, yet their translation into clinically interpretable visual forms remains underdeveloped. This article presents a study of conceptual dashboard design for digital twins in arthroplasty. Using a design science research approach, five iterative prototype cycles were conducted to examine how patient similarity, expected outcomes, and clinical pathways may be represented in dashboard form. The work was informed by the MIMIC-IV database and drew on cluster analysis, event-log-based pathway analysis, dashboard design principles, and cognitive walkthrough evaluation. Three complementary design concepts were developed: a cluster-based twin view, a pathway-based twin view, and an integrated dashboard combining both perspectives. Among these, the integrated concept offered the clearest representation of the digital twin by linking baseline patient characteristics, similarity groupings, and likely treatment trajectories within one navigable interface for clinical interpretation and use. The findings suggest that digital twins in arthroplasty may be communicated more effectively through structured dashboards than through isolated graphics or purely technical models, while also indicating the value of a shared conceptual framework for future clinically oriented visualization in healthcare.