Abstract

Background: The recent pandemic has shown its different shades across various solicitations, especially in the healthcare sector. It has a great impact on transforming the traditional healthcare architecture, which is based on the physical approaching model, into the modern or remote healthcare system. The remote healthcare approach is quite achievable now by utilizing multiple modern technological paradigms like AI, Cloud Computing, Feature Learning, the Internet of Things, etc. Accordingly, the pharmaceutical section is the most fascinating province to be inspected by medical experts in restoring the evolutionary healthcare approaches. COVID-19 has created chaos in the society for which many unexpected deaths occur due to delays in medication and the improper prognosis at an irreverent plan. As medical management applications have become ubiquitous in nature and technology-oriented, patient monitoring systems are getting more popular among medical actors.

Method: The Internet of Things (IoT) has achieved the solution criteria for providing such a huge service across the globe at any time and in any place. A quite feasible and approachable framework has evolved through this work regarding hardware development and predictive patent analysis. The desired model illustrates various approaches to the development of a wearable sensor medium that will be directly attached to the body of the patients. These sensor mediums are mostly accountable for observing body parameters like blood pressure, heart rate, temperature, etc., and transmit these data to the cloud storage via various intermediate steps. The storage medium in the cloud will be storing the sensor-acquired data in a time-to-time manner for a detailed analysis. Further, the stored data will be normalized and processed across various predictive models.

Results and Conclusion: The model with the best accuracy will be treated as the resultant model among the numerous predictive models deployed in the cloud. During the hardware development process, several hardware modules are discussed. After receiving sensor-acquired data, it will be processed by the cloud's multiple machine-learning models. Finally, thorough analytics will be developed based on a meticulous examination of the patients' cardinal, essential, and fundamental data and communicated to the appropriate physicians for action. This model will then be used for the data dissemination procedure, in which an alarm message will be issued to the appropriate authorities.

Graphical Abstract