Scalable AI-Enabled Healthcare Systems: Strategic Patient Segmentation and Clinical Intelligence

Abstract

The growing demand for personalized, efficient, and data-driven healthcare has underscored the need for scalable artificial intelligence (AI) systems capable of supporting strategic decision-making in clinical settings. This study presents an integrated AI-enabled healthcare framework focused on strategic patient segmentation and real-time clinical intelligence. Utilizing electronic health records, streaming data from wearable devices, and clinical notes, the framework combines supervised and unsupervised machine learning algorithms for risk prediction and patient clustering. Gradient Boosting and Random Forest models demonstrated high predictive accuracy (AUC-ROC > 0.91), while K-Means clustering effectively segmented patients into clinically meaningful groups. Principal Component Analysis (PCA) and multivariate statistics confirmed the distinctiveness of patient cohorts in terms of age, comorbidity, readmission, and mortality. Additionally, a real-time clinical intelligence module, supported by Apache Kafka and Spark, delivered timely decision support alerts and was rated highly by clinicians for usability and usefulness. The findings validate the feasibility and impact of deploying scalable AI systems to enhance care precision, optimize resource allocation, and support proactive clinical interventions. This research contributes to the growing body of evidence advocating for responsible AI integration in healthcare, offering a blueprint for future implementations in diverse medical environments.