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IntroductionDigital twins of patients are virtual models that can create a digital patient replica to test clinical interventionsin silicowithout exposing real patients to risk. With the increasing availability of electronic health records and sensor-derived patient data, digital twins offer significant potential for applications in the healthcare sector. MethodsThis article presents a scalable full-stack architecture for a patient simulation application driven by graph-based models. This patient simulation application enables medical practitioners and trainees to simulate the trajectory of critically ill patients with sepsis. Directed acyclic graphs are utilized to model the complex underlying causal pathways that focus on the physiological interactions and medication effects relevant to the first 6 h of critical illness. To realize the sepsis patient simulation at scale, we propose an application architecture with three core components, a cross-platform frontend application that clinicians and trainees use to run the simulation, a simulation engine hosted in the cloud on a serverless function that performs all of the computations, and a graph database that hosts the graph model utilized by the simulation engine to determine the progression of each simulation. ResultsA short case study is presented to demonstrate the viability of the proposed simulation architecture. DiscussionThe proposed patient simulation application could help train future generations of healthcare professionals and could be used to facilitate clinicians’ bedside decision-making.