Introduction

Critical illness management in Kenya’s public hospitals is crippled by severe ICU bed shortages and a reliance on manual, reactive referral processes [1]. This systemic lack of real-time visibility across facilities leads to delayed patient transfers, poor resource utilization, and preventable loss of life. 

The core challenge is reactivity: clinical decisions and resource allocations are made only after overcrowding has occurred, severely limiting effective response during patient surges. 

ICUConnect addresses this gap through a closed-loop, AI-driven platform that transforms critical care coordination from reactive to proactive. By combining a centralized referral system with real-time ICU occupancy monitoring, the platform’s embedded machine learning model analyzes admission and discharge patterns to forecast bed demand up to a week in advance. This predictive capability triggers early surge alerts (e.g., at 80% occupancy), enabling healthcare teams to anticipate crises, optimize resource allocation, and coordinate life-saving transfers more efficiently across the hospital network.