P2D: An Efficient Patient-to-Doctor Framework for Real-Time Health Monitoring and Decision Making

Today, diseases and illnesses are becoming the most dangerous enemy to humans. The number of patients is increasing day after day accompanied with the emergence of new types of viruses and diseases. Indeed, most hospitals suffer from the deficiency of qualified staff needed to continuously monitor patients and act when an urgent situation is detected. Recently, wireless body sensor network (WBSN) has been considered as an efficient technology for real-time health-monitoring applications. It provides a low cost solution for hospitals, performs a relief for staff and allows doctors to remotely track patients. However, the huge amount of data collected by sensors produce two major challenges for WBSN: the quickly depletion of the available sensor energy and the complex decision making by the doctor. In this article, we propose an efficient Patient-to-Doctor (P2D) framework for real-time health monitoring and decision making. P2D works on two levels: sensors and coordinator. At the sensor level, P2D allows to save the sensor energy, by adapting its sensing frequency, and to directly detect any abnormal situation of the patient. Whilst, at the coordinator level, P2D allows to store an archive for each patient, predict the patient situation during the next periods of time and make a suitable decision by the doctors. We conducted a set of simulations on real health data in order to show the relevance of our platforms compared to other existing systems.

Automated summary

With diseases and illnesses posing a growing threat to humans, hospitals are facing a shortage of qualified staff for continuous patient monitoring. Wireless body sensor networks (WBSN) are seen as a cost-effective solution for real-time health monitoring, though they also present challenges such as energy depletion and complex decision-making for doctors. The article proposes an efficient Patient-to-Doctor framework that operates at sensor and coordinator levels, enabling energy savings and timely detection of abnormalities at the sensor level, while at the coordinator level, it facilitates patient data storage, prediction, and decision-making by doctors. Simulation studies on real health data demonstrate the relevance of the proposed framework compared to existing systems.