Table 2.

DT^a applications in operational efficiency and resource management.

Problem (and root causes)	Inputs	Processes	Outputs
Unpredictable patient volumes Difficulty in dynamically adapting workflows in high-stakes situations Limited real-time operational insights that lead to inefficiencies and bottlenecks	Data: Patient data Historical trends Sources: Hospital information systems	Simulation: the DT can simulate hospital scenarios. By modeling these patterns, DTs can analyze how variations in patient flow affect wait times, bed availability, staffing requirements, and equipment usage, giving administrators foresight into potential bottlenecks Optimization: based on simulation results, the DT can recommend ideal staffing levels, identify underused or overbooked resources, and schedule maintenance during low-demand periods Real-time decision-making: the DT can suggest workflow shifts, such as rerouting noncritical cases, and improve discharge planning to free up beds efficiently	Optimized patient flow and resource Real-time adjustments in staffing and equipment usage based on patient arrivals Improved discharge planning and maintenance scheduling to ensure optimal bed availability and patient throughput Efficient crisis management strategies

^aDT: digital twin.