Swiss emergency care has been in the spotlight not only since the outbreak of the Corona pandemic. Emergency departments throughout the country are coming under increasing pressure due to continuously rising patient numbers – this is also referred to as the emergency department crowding effect. It therefore seems inevitable to adapt the structure of emergency care in Switzerland as well as internationally. To this end, in a research project funded by Innosuisse, partners from applied research (BFH, FHS St.Gallen), emergency medicine (Inselspital, SGNOR[1]) and consulting (walkerproject) are using the computational design thinkingmethod to develop sustainable process and structural optimisation strategies for this care sector. Megatrends such as globalisation and the digital transformation have led to the fact that we now live in a VUCA world. The VUCA characteristics – volatility, uncertainty, complexity and ambiguity – not only affect the world as a whole, but also apply in particular to important subsystems such as the health or economic system. A crucial consequence of VUCA is that traditional analysis and problem-solving methods (including regression analyses and business strategy tools such as Porter’s Five Forces) are failing more and more frequently, as these tools usually take a static and linearised view of important issues. This is at odds with VUCA assumptions, which assume mostly dynamic and non-linear (complex) interactions. The phenomenon of crowded emergency departments – internationally known as Emergency Department (ED) crowding (Asplin et al., 2003; Hoot and Aronsky, 2008) – illustrates this fact impressively. It is one of the prototypical dynamic-complex problems in health services research: “dynamic” because the severity of the problem changes over time, and “complex” because it is caused by a multitude of developments that can also influence each other – such as demography (ageing society), migration, pressure at the workplace, mobility, “convenience”, patients’ expectations, etc. (Schoenenberger et al., 2008). (Schoenenberger et al., 2016). These problem characteristics are also responsible for the fact that ED crowding has for years shown itself to be extremely immune to optimisation strategies developed with mostly traditional (reductionist) methods (Sterman, 2000). To address the dynamics and complexity of crowded emergency departments, an interdisciplinary consortium of applied science (BFH, FHS St.Gallen), emergency medicine (Inselspital, SGNOR) and consulting (walkerproject) is relying on a holistic and user-oriented approach from computational design thinking in the development of new procedural and structural optimisation strategies for this care sector (Menges and Ahlquist, 2011). In the research project funded by Innosuisse, new emergency process models from design thinking and lean management will be transferred into operational computer simulation models in order to analyse their consequences for emergency patients and staff over time. For this purpose, a virtual twin(digital twin) of the university emergency centre at Inselspital Bern will be created using discrete-event computer simulation technology (Fishman, 2013). In addition, this project also pays special attention to the systemic embedding of emergency departments. In collaboration with SGNOR, so-called context models are being developed (Grösser, 2017), which depict visions of the future design of different types of emergency departments. For the development of the context models, it is essential to take into account the various interactions between emergency care, hospital care, primary care, etc. The aim is to use the consolidated findings of the project to develop a new context model. The aim is to derive effective and sustainable optimisation strategies for Swiss emergency care from the consolidated findings from the virtual twin and the context models.

References

1. Asplin, B. R., Magid, D. J., Rhodes, K. V., Solberg, L. I., Lurie, N., & Camargo Jr, C. A. (2003). A conceptual model of emergency department crowding. Annals of emergency medicine, 42(2), 173-180.
2. Fishman, G. S. (2013). Discrete-event simulation: modeling, programming, and analysis. Springer Science & Business Media.
3. Grösser, S. N. (2017). Complexity management and system dynamics thinking. In Dynamics of long-life assets (pp. 69-92). Springer, Cham.
4. Hoot, N. R., & Aronsky, D. (2008). Systematic review of emergency department crowding: causes, effects, and solutions. Annals of emergency medicine, 52(2), 126-136.
5. Menges, A., & Ahlquist, S. (2011). Computational design thinking: computation design thinking. John Wiley & Sons.
6. Schoenenberger, L. K., Bayer, S., Ansah, J. P., Matchar, D. B., Mohanavalli, R. L., Lam, S. S., & Ong, M. E. (2016). Emergency department crowding in Singapore: Insights from a systems thinking approach. SAGE Open Medicine. https://doi. org/10.1177/2050312116671953
7. Sterman, J. D. (2000). Business dynamics: systems thinking and modeling for a complex world. McGraw-Hill Education.
8. 1] Swiss Society for Emergency and Rescue Medicine (SGNOR)