A Predictive model for blood demand and supply: a case of obstetric emergencies at Kenyatta National Hospital

Abstract

Maternal health is still a very critical concern in Kenya, with obstetric emergencies posing considerable challenges to maternal health and mothers’ well-being. One key factor that contributes to maternal mortality is the unavailability of blood during obstetric complications, which is essential for saving lives during emergencies related to childbirth. Unfortunately, many lives have been lost under the circumstances due to a lack of timely information regarding blood requests and donations. These delays in finding blood to manage such situations more effectively cause loss of life. The study aimed to develop a predictive web-based model to monitor blood demand and supply and ensure blood is available to save the lives of mothers and newborn babies during obstetric emergencies using Kenyatta National Hospital (KNH) as a case study. This will be achieved through improved blood bank management, monitoring blood availability and providing timely information to blood stakeholders, minimizing delays in blood donation and supply requests during obstetric emergencies. This will also help reduce maternal and infant mortality cases, thus improving maternal health outcomes. If successful, the proposed solution will go a long way in improving blood donation practices in the healthcare system. The model leveraged modern application development technologies and tools, including Predictive Analytics, Artificial Intelligence, and Machine Learning libraries, to forecast blood demand and supply based on historical data. The research adopts an experimental design to gain in-depth insights into the current practices, challenges, and patterns in blood management. This approach facilitated the identification of critical variables influencing blood demand and supply, leveraging both qualitative and quantitative data to make correct blood demand versus supply predictions. By exploring existing systems, healthcare workflows, and historical data, the study will provide a foundation for constructing an effective predictive model tailored to the unique requirements of obstetric care.

Keywords: Obstetric Emergency, Maternal Health, Blood Donation, Predictive Analytics.