Data-Driven Decision Support for Scalable Healthcare Using Cloud and Behavioral AI Models

Abstract

Healthcare systems today are growing in both complexity and scale, and with that comes a real need for smarter, more adaptive decision-making tools. This study looked at how cloud computing and behavioral AI can be brought together to build a decision support system that works at scale and responds to real-world clinical demands. The idea was to pull in a wide range of patient data, from EHRs and genetic profiles to lifestyle habits and behavioral signals, and use that to generate clinical insights that are not only timely, but also tailored to each individual. To make that possible, we built distributed data pipelines on a cloud-based setup and layered in several machine learning models, including Random Forest, XGBoost, and behavioral clustering methods. We tested these across a mix of clinical scenarios, things like predicting whether a patient will stick to their treatment, forecasting the chance of a disease coming back, or helping triage decisions in real time. Key metrics like ROC-AUC, F1-score, and precision-recall were used to measure performance. What made the system stand out was how behavioral AI added extra context to patient choices, which helped shape more meaningful, personalized intervention recommendations. Compared to traditional hospital-based tools, the new system showed clear improvements, not only in how accurate the predictions were, but also in how fast and flexibly they were delivered. It handled large-scale data, supported secure collaboration across different locations, and responded in real time without losing reliability. In short, bringing together behavioral AI and scalable cloud infrastructure doesn’t just make decision-making more precise, it also opens up a new path for delivering care that adapts to both the patient and the system around them.