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Principal Investigators: Dr. Nathan Kenya-Mugisha and Dr. Matthew Wiens

Purpose

In many African countries, paediatric post-discharge mortality following in-hospital treatment for severe infectious illness is higher than in-hospital mortality (5-8%). Risk algorithms can be used to help health workers identify those most vulnerable to poor post-discharge outcomes. They can also assist health workers in providing personalised discharge counselling and recommending effective follow-up care. This can improve overall system efficiency. While this approach has shown promise among general paediatric populations, no research has addressed issues of post-discharge morbidity and mortality within the refugee context, where unique vulnerabilities exist.

This study aims to develop and validate the Smart Discharges approach to improve outcomes among children in refugee settings, and ultimately to build a generalisable and inclusive solution to improving paediatric post-discharge outcomes.

Dr. Nathan Kenya-Mugisha, PI

WALIMU

The ability to identify children at-risk of poor post-discharge outcomes is of paramount importance, especially within vulnerable refugee settings. This critical information enables us to allocate limited resources towards improving the hospital-to-community transition more efficiently. Such programs not only save lives and resources but are more likely to be scalable in economically strained environments.

Expected Outcomes

This study will validate the Smart Discharges prediction model for post-discharge mortality among children living in refugee settings and generate evidence on the epidemiology of paediatric post-discharge morbidity and mortality in the context of a refugee setting.

The long-term goal is to integrate the predictive algorithms for refugee and non-refugee children into a single platform that can be implemented in any Ugandan hospital to support quality improvement on discharge and post-discharge care. Ultimately, this work will lead to improved quality of post-discharge care and reduced post-discharge mortality for children living in refugee settings in Uganda.

Smart Discharges platform launched as part of Smart Quality Improvement (Smart QI) platform

Smart Discharges is a digital health programme within the Smart QI Platform. Smart QI uses data-driven risk prediction to target interventions towards mothers, newborns, and children during the patient journey – from triage to post-discharge. The goal is to provide the right intervention at the right time to the right patient, envisioning the development of health system efficiency through simple and cost-effective digital innovations.

View the platform
A Smart Discharges research nurse measures a patient’s oxygen saturation (SpO2) using a pulse oximeter connected to a tablet. Key clinical and demographic variables collected in the tablet by nurses are used to predict a child’s risk of post-discharge morality and morbidity. Nurses then select an appropriate risk mitigation strategy based on the patient’s risk score. Credit: Micah DeKorne, Designed4

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