Real Time Nursing Management System for Health Care Industry by using Xenomai Kernel

Background/Objectives: Doctors and patients are the primary stake-holders in patient care life-cycle. The vital role of nursing assistants and other supporting medical staffs like cleaning is often neglected. The goal of this work is to create a bridge-module between nursing staff and patients to provide best standard class patient care. Methods/Statistical Analysis: This paper discusses the issues in nursing management and proposes methodology involving real-time intelligent embedded system and analyses its outcomes. The proposed solution comprises of Radio Frequency (RF) and wearable technology to bridge the communication between patient and care-giver and data analytics modules to store, analyze and access the in-sights. The Xenomai Real Time Kernel on a System on Chip (SOC) that is imperative for real-time scheduling of critical tasks. This system also got capability to store and analyzes the urgency records from patients, which are critical for identifying the inefficiency in process or resource utilization. Findings: Existing Nursing management systems are software based solutions which lacks in maintain the communication from the patients in real-time. Proposed system provides facility to patient to request for service and any time. This also stores the services in its database, which can be utilized for prediction by performing data analytics modules. By predicting the patient’s service, nursing staff can prepare itself in advance for serving patient services, Hence it is providing support in utilizing the resources effectively. Conclusion/Improvements: In this paper, the gateway is designed to act as a central monitoring system, which provides worthy addition to present hospital management systems. RF Remote and watches are used to provide communication between patients and nursing care. Both hardware and software design of gateway is discussed briefly with series of outputs, which proves that proposed systems is feasible and efficient in real-time scenario.