Machine learning-powered patient risk assessment system for proactive healthcare management and resource allocation
A healthcare network struggled to identify high-risk patients before complications arose, leading to expensive emergency interventions and poor patient outcomes. Their reactive approach to patient care resulted in higher costs and missed opportunities for preventive treatment, particularly for chronic conditions like diabetes and cardiovascular disease.
We developed a comprehensive risk stratification model using gradient boosting algorithms that analyzed patient demographics, medical history, lab results, medication adherence, and social determinants of health. The system provided risk scores across multiple disease categories and recommended appropriate intervention levels for each patient population.
The system achieved 89% accuracy in predicting patient health deterioration within 6 months, enabling proactive care management. Healthcare costs were reduced by 35% through early intervention and optimized resource allocation. The model identified 60% more at-risk patients compared to traditional screening methods, significantly improving preventive care outcomes.