A Lean-based Digital Twin model for planning and governance in secondary and tertiary hospital networks

Healthcare systems face growing pressure and recurrent capacity-demand imbalances across hospital networks. While Lean Healthcare improves patient flow, most implementations remain confined to departmental silos and individual facilities. Simultaneously, Digital Twin (DT) applications in healthcare predominantly focus on precision medicine or isolated unit optimization, leaving a gap in system-level resource management. This study aims to propose a Lean-grounded DT model to support integrated activity and capacity planning across multi-provider hospital networks.

Situated within the planning phase of a broader Action Research approach, this study translated Lean process-based frameworks into formal DT requirements. The model's scope and functional capabilities were derived from a longitudinal synthesis of Lean improvement projects conducted in over 50 hospitals. To strengthen structural integrity and practical relevance, the framework underwent expert-informed review with a purposeful panel of senior healthcare leaders, including CEOs and Medical Directors with more than 20 years of experience, to iteratively refine the decision-support layer and ensure alignment with territorial governance challenges.

We propose a four-layer conceptual DT architecture operating at both individual-hospital and network management level. The model represents four interrelated macro-processes (ambulatory, surgical, inpatient, and emergency) that function as interconnected systems across the territorial network. It standardizes core entities, operational variables (demand, activity, capacity and performance), and cross-process transition probabilities into a unified virtual representation. The resulting decision-support layer enables scenario analysis for medium-term tactical contracting, bottleneck detection, and strategic resource allocation across multiple providers.

By integrating Lean process orientation with the DT paradigm, this model contributes to extending hospital management beyond traditional hospital-centric optimization. It offers a data-driven framework intended to support territorial health authorities in shifting from reactive, historically based contracting to more proactive, value-based network management. This model provides an architectural basis for future empirical implementations of Digital Twins in complex territorial health networks.