The healthcare sector faces complex challenges exacerbated by tight timelines and fragmented data. Uncertainty, underutilized resources, overcrowding and inadequate information are common problems for decision-makers. In resolving such matters, a Digital Twin, a detailed digital replica of the healthcare system, offers a promising solution. This technology models patient inflows and determines congestion levels and resource and service use to simulate, forecast, and optimize healthcare behaviors. We show the reliability of our system by modelling a complex organization such as one of the Local Health Authority (ASL) of Tuscany. Our Digital Twin system could enable proactive and deliberative-driven decisions by supporting the evaluation of system impacts and optimizing resource utilization. Moreover, the Digital Twin enhances system resilience while improving the decision-making process with the additional data provided through feedback. Such a tool may enable better strategic and optimal resource use.