A health digital twin framework for discrete event simulation based optimised critical care workflows.

Area of Science:

• Healthcare technology
• Digital health
• Critical care medicine

Background:

• Digital twins are increasingly adopted in healthcare, especially for precision medicine.
• Discrete Event Simulation is a key methodology for modeling healthcare processes.
• Optimizing critical care workflows is essential for patient safety and operational efficiency.

Purpose of the Study:

• To present an integrated methodology of digital twins and Discrete Event Simulation for optimizing critical care workflows.
• To propose a novel dual-layer architecture for monitoring physical and conceptual entities in a Critical Care Unit.
• To establish a real-time treatment workflow tracking methodology using Azure cloud.

Main Methods:

• Integration of digital twin technology with Discrete Event Simulation.
• Development of a dual-layer architecture for real-time monitoring.
• Implementation using Azure cloud services for workflow tracking.

Main Results:

• The proposed methodology successfully tracked 72% of staff-performed tasks in real-time by reviewing observation forms.
• Demonstrated the potential for real-time optimization of critical care workflows.
• Highlighted the feasibility of using cloud-based solutions for healthcare process monitoring.

Conclusions:

• Digital twins, integrated with Discrete Event Simulation, offer significant potential to enhance precision care in critical settings.
• The developed methodology can bridge the gap between actual and ideal clinical practices.
• Real-time tracking of workflows improves patient safety, operational efficiency, and sustainability in critical care.