With the advent of the Internet of Things (IoT), various interconnected objects can be used to improve the collection and the process of vital signs with partially or fully automatized methods in smart hospital environment. The vital signs data are used to evaluate patient health status using heuristic approaches, such as the early warning scoring (EWS) approach. Several applications have been proposed based on the early warning scores approach to improve the recognition of patients at risk of deterioration. However, there is a lack of efficient tools that enable a personalized monitoring depending on the patient situations. This paper explores the publish-subscribe pattern to provide a self-adaptative early warning score system in smart hospital context. We propose an adaptative configuration of the vital sings monitoring process depending on the patient health status variation and the medical staff decisions.
Changes in vital signs are an important indicator of physiological decline and provide opportunities for early recognition and intervention. The collected vital signs data can be evaluated using several approaches such as the Early warning score (EWS) approach to predict the risk level of patients. By exploring the Internet of things (IoT), vital signs monitoring solutions are automated based on various medical devices and sensors. However, there is a lack of efficient tools that enable an adaptative monitoring depending on the patient situations. This article explores the IoT technologies to provide an EWS system in smart hospital situation. The proposed solution presents an adaptative configuration of the vital signs monitoring process depending on the patient's health status variation and the medical staff decisions. Also, an intelligent notification mechanism that reduces the delay of the medical staff intervention in the case of risk detection is proposed. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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