An Innovative Monitoring system for PAediatrics in Low-resource settings: an Aid to save lives.
- To further develop our current tablet-assisted monitoring system that is suitable for paediatric in-hospital care in LRS to create a monitoring system that provides a real-time prediction of critical events through predictive algorithms based on vital signs, optionally supplemented with clinical data and biomarkers allowing timely, life-saving interventions.
- To develop algorithms that predict critical illness based on vital signs and to enhance the accuracy of these by combining vital signs with clinical data and/or biomarkers.
- To conduct extensive implementation research to identify key barriers to implementation of vital signs monitoring in LRS and to address these barriers appropriately.
- To design a multi-country RCT assessing the impact of the monitoring device and implementation strategy on in hospital paediatric survival in LRS.
More than 3 million children in low-resource settings (LRS) die annually due to contextual constraints in LRS healthcare systems that hamper the widespread supply of high-quality healthcare. Many of these deaths are advanced stages of poverty-related diseases that are recognised too late to be treated effectively while treatment usually is available. Monitoring of vital signs is essential for early detection of critical illness as vital signs change early in the course of the disease.
Shortage of (qualified) staff and lack of suitable equipment are the main bottlenecks to monitor these children adequately during admission. Current monitoring systems widely used by clinicians in high-resource settings are not suitable for LRS due to their high costs and poor compatibility with LRS settings.
The IMPALA project will address this problem by developing an affordable, durable, and user-friendly monitoring system (IMPALA) for hospitalised children in LRS. By combining innovative sensors, machine learning algorithms and point-of-care biomarkers we create a smart, yet simple, monitoring system that enables health workers to timely detect and predict critical illness.
AIGHD Research Lead
Dr. Job Calis
Training and Research Unit of Excellence (TRUE)
Imperial College of Science Technology and Medicine (ICL)
GOAL 3 BV
Kamuzu University of Health Sciences – College of Medicine (KUHeS)
Malawi University of Business and Applied Sciences (MUBAS)
National eHealth Living Lab (NeLL)