Multi-sources data fusion framework for remote triage prioritization in telehealth

Area of Science:

• Biomedical Engineering
• Health Informatics
• Remote Patient Monitoring

Background:

• Healthcare systems face challenges in providing timely and effective patient services.
• Telemonitoring systems offer potential for improved patient care through remote access.
• Integration of smart devices and algorithms can enhance healthcare delivery.

Purpose of the Study:

• To propose a novel framework, the Multi Sources Healthcare Architecture (MSHA), for advanced healthcare applications.
• To enhance the efficiency and scalability of healthcare telemonitoring services.
• To improve remote patient triaging and prioritization using multi-source data.

Main Methods:

• Developed the Multi Sources Healthcare Architecture (MSHA) framework.
• Integrated multi-source data including ECG, SpO2, Blood Pressure sensors, and text-based inputs from Wireless Body Area Networks.
• Employed data fusion techniques and prioritization algorithms within the MSHA framework.
• Simulated the MSHA algorithm in the base station tier of a telemonitoring system.

Main Results:

• The MSHA framework demonstrated high accuracy in remote patient prioritization and triaging.
• Multi-source data fusion was shown to be effective in telemonitoring healthcare services.
• Simulation results indicated the superiority of the MSHA algorithm over conventional methods for classifying and prioritizing patients with chronic heart diseases.

Conclusions:

• The proposed MSHA framework effectively supports advanced healthcare applications by integrating diverse data sources.
• MSHA enhances remote triaging and prioritization, leading to improved healthcare scalability and efficiency.
• The framework shows significant potential for application in real-world healthcare telemonitoring scenarios, particularly for chronic disease management.