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Enabling evidence-based design: guidelines for the development of integrated inpatient and building digital twin model

Abstract

Evidence demonstrates a direct correlation between temperature increases and hospital inpatients' excess deaths. Events such as heat waves have caused significant and exponential increases in excess deaths when inpatients are exposed to prolonged periods of higher temperatures. Evidence also shows an increase in the number of heat waves globally. Efforts are being made to improve healthcare efficiency, with many areas yet to be developed to increase efficiency. However, people still spend a lifetime inside poorly designed, built and maintained buildings that have not been designed to accommodate (climate) changes. As a result, healthcare buildings can often be too hot, too cold, and too damp, with high levels of CO2. Excess deaths are an example of the consequences of not looking after our healthcare environments adequately, not reacting quickly enough, and not preventing problems before they occur. This proposal aims to address the issues caused by climate change and hospital unpreparedness by investigating the development of integrated solutions based on digital twins. The narrative in this proposal is informed by a current IAUUSP research project (FAPESP 20/12141-1 and 22/00391-9), exploring the evidence-based design of healthcare facilities in the Covid-19 pandemic control context. This ongoing research addresses the physical space of the University Hospital of the Federal University of Sao Carlos, Brazil. The results have demonstrated that although simulation is a compelling approach to understanding various aspects of building performance, there are limitations to the extent that these simulation models accurately incorporate many independent variables related to care provision. Therefore, this proposal aims to develop a multidisciplinary network of academics to discuss how digital twin technology can support a more accurate and effective approach to delivering (preventive) care. In essence, the team will explore the feasibility of using digital twins for monitoring patients' responses to changes in building performance. (AU)

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