Emergency departments (EDs) are vital components of healthcare systems, often operating under extreme pressure, especially during seasonal peaks of respiratory diseases like influenza, Respiratory Syncytial Virus (RSV), or COVID-19. These peaks lead to significant overcrowding, prolonged waiting times, and increased strain on clinical staff, which compromise patient outcomes and system efficiency. The challenge lies in dynamically allocating resources and predicting patient flow with enough accuracy to maintain operational stability. Digital twin (DT) technology, a virtual real-time representation of physical systems, offers a transformative solution. By mirroring the ED operations and continuously synchronising with real-world data, digital twins can simulate various scenarios and inform optimal decision-making strategies. This paper presents the application of digital twins for shortening waiting times in EDs during respiratory disease peaks. First, we characterized the patient journey within the ED using the Supplier-Input-Process-Output-Customer (SIPOC) diagram. Secondly, we performed an input data analysis and then modelled the ED through a DT designed in ARENA® software. After this, we validated the model by conducting a 1-sample t test on the waiting time for treatment in ED (3–5 triaged patients). Finally, we implemented a what-if analysis considering two scenarios: i) increasing the number of beds and general doctors, ii) reducing delays caused by clinical labs in delivering test results. The proposed approach was verified in a European hospital group during one of the first COVID-19 waves. The results showed that the treatment waiting time in 3–5 triaged patients (4.682 h) can be significantly lessened if both scenarios are applied.