Project 460680

Sepsis occurs when a severe infection leads to organ damage and is most common cause of death and disability in children globally. Most deaths occur in poorer countries and are preventable with timely and appropriate treatments and follow-up care after leaving the hospital. Our team has developed two prediction models that assign a risk score to a child with sepsis, based on information collected by nurses at the hospital. Nurses use these risk scores to determine the level of care a child requires at different points of care. In one model, the nurse uses the risk score to determine what care a child requires after arriving at a hospital with sepsis. In the second model, the nurse uses the risk score to determine what care the child requires after going home following treatment for sepsis. Both models use some of the same information. Combining these models into one program so that data only needs to be collected once would be more efficient for nurses and could improve overall care for a child. However, different hospitals use different digital systems. We want to create a single program that can communicate with each of these systems. We will host a workshop with partners in East Africa and Canada to develop an approach for combining our models in one system that can be used in many different health settings. We will bring together partners from the companies that create digital systems used by hospitals, policy-makers, health workers, and researchers, and discuss challenges and potential solutions. We will use new recommendations from the World Health Organization on creating smart guidelines for digital systems to help develop our approach. Together, we will create a plan for developing and using this approach to improve care for vulnerable East African children. In the long-term we will be able to adapt this approach to low-resource settings globally, including in Canada.