EW: MEDICAL
insufficient investment and the increasing costs associated with providing healthcare, means countless hospitals and clinical facilities across the world are operating at or beyond capacity. The consequences of stretched healthcare institutes are significant, as they leave countries without the necessary surplus bandwidth to handle large-scale emergencies such as natural disasters or pandemics. Furthermore, this can needlessly raise mortality rates in cases where urgent treatment is crucial but has to be postponed due to a lack of resources. There is an impetus for healthcare providers and governments to improve healthcare services, but this is hard to achieve. Increasing the size of the workforce is an obvious solution, but it poses a significant challenge due to the global shortage of healthcare professionals: The World Health Organization (WHO) predicts a global shortage of 10 million healthcare workers by 2030. Many healthcare services are trying to figure out how to accomplish more with limited resources. Connected and intelligent MIoT devices can offer an efficient and technol- ogy-driven solution to this problem. They can allow for more automated monitoring and optimised resource allocation and can help to minimise human intervention in routine tasks, resulting in cost reduction
and increased operational efficiency. The role of Edge computing in MIoT devices Currently, approximately
30% of the world’s data volume is generated by the healthcare sector (RBC). Data is an indispensable tool for advancing patient diagnostics, but it must be processed quickly and accurately for it to be useful. With Edge computing, data processing is moved to the device itself. Unlike centralised Cloud computing, where data is sent to a remote data centre for analysis, Edge computing allows MIoT devices to process information locally, reducing latency, enhancing data security, and improving the real-time capabilities of medical devices. In medical environments, it is also important to consider wireless local area network (WLAN) traffic. Most healthcare facilities already possess a multitude of Wi-Fi-enabled devices, potentially leading to congested networks. By employing AI algorithms and machine learning (ML)
Figure 2. Analog Devices MAX78002 microcontroller. (Source: Mouser Electronics)
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