Developing a Real-Time Data Analytics Platform for Healthcare Resource Management in Tertiary Hospitals in Nigeria

Tertiary hospitals across Nigeria suffer from inefficient resource allocation, including beds, operating theatres, and diagnostic equipment, because patient flow data is not systematically analysed in real time to support operational management decisions. This study developed a real-time data analytics platform for healthcare resource management applicable to Nigerian federal teaching hospitals. A platform architecture design and prototype implementation methodology was adopted, using University College Hospital Ibadan as the reference institution. A data integration layer was designed to extract patient data from existing HMIS databases (MySQL-based LMIS and the open-source OpenMRS EMR) via REST APIs. An Apache Kafka streaming pipeline processed admission, discharge, and diagnostic request events in real time. A Python analytics module calculated key operational metrics including bed occupancy rate, average length of stay, diagnostic equipment utilisation rate, and emergency department waiting time, updated every five minutes. A Grafana dashboard provided real-time visualisation for hospital administrators and department heads. The system was prototyped and tested using 90 days of de-identified historical patient flow data from UCH Ibadan. Dashboard metrics calculated from the historical dataset matched manually computed reference values within 0.5 percent. System performance at 200 simulated concurrent event streams showed sub-10-second end-to-end processing latency. The study estimates a potential 12 to 18 percent improvement in bed utilisation efficiency from implementing real-time occupancy management, based on analysis of avoidable bed idle periods in the historical dataset.

Keywords: healthcare analytics, resource management, real-time dashboard, tertiary hospital, South West Nigeria