Newer Devices and Remote Diagnostics and Monitoring Based on IOT

Total health expenditure (THE) budget has been on the rise over the past few decades, which incurs anywhere between 3% and 15% of the total gross domestic product (GDP) of a country. A significant component of the health budget is spent on health care human resource which includes physicians, nurses, technicians, medical practitioners and so on (Bedir 2016). Treating an illness requires expertise, which only an experienced doctor or a surgeon can fulfil. However, there are various other components where costs can be reduced, especially for diagnostics (Prakashan et al. 2023). The recent pandemic has shown the world, situations where physical contact among patients and doctors became next to impossible. These situations have led to the discoveries and focus towards developing remote diagnostic devices that could save time, expenditure, travel, physical contact, and at the same provide information at lighting fast speeds. These devices are based on the same principles of testing of parameters such as images, blood, bacteria, biomedical signals, joint movements and so on. Reliability of these devices is largely dependent upon the method and quality of data acquisition and interpretation (Mavrogiorgou et al. 2019). Data acquisition methodology is dependent upon the parameter of the disease that needs to be captured which is often based upon the actual principle on which the healthcare diagnostic facility equipment is based. Based on the reliability of data collection from this portable device, it can either be sent to a remotely located expert or a physician for diagnosis and interpretation. Hence, the physicians’ physical location becomes redundant. A further advancement into this process could be the development of a machine learning-based algorithm using a relevant dataset that would provide the diagnosis automatically without intervention of an expert (Ruskin et al. 2004). Another option for providing validated diagnosis could be sending the machine learning-based diagnosis to an expert for approval, thereby, providing a further authenticated result. Therefore, device innovators or fabricators would opt for their preferred method of reporting and diagnosis. However, for satisfying the patient requirement, real-time assessment and remote accessibility, a machine learning validated algorithm-based device is a necessity.