Infection Spread and Outbreaks Support with Spatial-Temporal Visualization Tool for Hospitals

Hospital-acquired infections (HAIs), especially those caused by multidrug-resistant bacteria, represent a critical challenge, increasing healthcare costs, hospital stays, and mortality rates. Monitoring HAIs requires integrating spatial-temporal data from patient records and microbiology results. However, current manual methods are time-consuming and error-prone. Although temporal factors are often considered, spatial patient distribution and hospital topological factors are frequently overlooked. Interactive information visualization provides a solution, combining diverse data sources to enhance understanding of spatial-temporal relationships. We aim to develop OBViz, an interactive visual tool employing spatial-temporal visualization techniques to analyze infection spread and hospital epidemic situations over time. Four user tasks relevant in HAIs control were defined, focusing on spatial-temporal pathogen localization and identifying outbreak origins. Using Unity 3D and C#, along with a simulated dataset of hospitalized patients experiencing an infection spread, we developed a visual interactive tool that integrates 3D hospital visualization for patients' individual monitoring, 2D visualization for tracking epidemiological indicators, and tabular view for detailed information. A user study with 14 healthcare personnel evaluated its usability, usefulness and interpretability. Interactivity and animations accurately depicted movements and infection processes, while known charts facilitated temporal understanding. Despite room for improvement in patient tracking (57.14% success rate), OBViz demonstrated strong potential for decision-making (91.43% success rate), healthcare education, and integration into clinical workflows (Post-Study System Usability Questionnaire result: 1.85). The tool's interactive spatial visualization and clear time control were preferred over more abstract methods, highlighting its utility in hospital epidemic analysis.