Toward Digital Twins for Human-in-the-loop Systems: A Framework for Workload Management and Burnout Prevention in Healthcare Systems

While digital twin (DT) research matured in the past decade, DT applications in human-in-the-loop (HITL) systems are nascent. HITL systems differ from engineered systems as they are sociotechnical systems (STSs) that rely on collaborating teams of humans for making safety-critical operational decisions. Healthcare systems (HSs), an archetypical STS, have recently attracted increased attention from DT research. Nevertheless, the current literature greatly overlooks the linkage between the system and the HITL - healthcare providers (i.e., doctors and nurses) in this case - particularly regarding the influence of work demands on HITL performance. This is problematic because provider performance plays a crucial role in the overall functionality and effectiveness of HSs. Taking providers’ interests and the long-term sustainability of HSs into account, this paper lays the foundation for a conceptual HITL workload DT for HSs. We provide an overview of the complex macroergonomic factors that influence provider workload using the systems engineering initiative for patient safety (SEIPS) framework and discuss workload quantification methods to operationalize this information. We then describe how DT modeling and twinning methods can be leveraged to collect and utilize relevant operational data for proactive management of provider workload. Finally, we discuss associated challenges such as data collection, establishment of necessary feedback mechanisms, and calibration of workload quantification methods. If successful, the proposed framework has the potential to effectively address a pressing national issue: provider burnout in HSs, while extending DT research to other STSs such as air and rail traffic control.