The Evolving Healthcare Stack: Merging Wearables, AI, and Patient Risk Prediction Models

Abstract

The way wearable tech and AI are coming together is quietly changing how we think about healthcare. We're starting to move beyond reactive care and into a space where early warnings and personalized interventions aren't just possible, they're practical. In this study, we explored how physiological data from wearables, when combined with solid machine learning models, can help flag patient risk more effectively, especially for chronic conditions like diabetes, cardiovascular issues, and complications that can lead to hospital readmission. We built a layered system that pulls in continuous biosignals from wearables and runs them through various predictive models. We used a mix of supervised algorithms like Random Forest, XGBoost, and logistic regression, along with some semi-supervised methods for situations where labeled data was sparse. The focus during feature engineering was on capturing time-based patterns, spotting deviations in trends, and incorporating context around the patient. We evaluated model performance using ROC-AUC, F1, and precision-recall, testing everything against carefully stratified clinical datasets. What stood out was how much better the models performed when wearable data was part of the picture. For instance, we saw a noticeable bump in accuracy when it came to early signs of irregular heart rate variability and blood glucose trends. XGBoost was the most consistent performer, with ROC-AUC scores often above 0.91. One of the more meaningful results came in readmission prediction: by adding time-sensitive wearable data, we improved the F1-score by 22% compared to using EHR data alone. This kind of improvement isn’t a minor tweak. It shows that wearable-informed AI systems could play a real role in shifting healthcare toward a more preventative, patient-centered model. Looking ahead, there's a clear need to focus on building real-time data pipelines, making sure data privacy is baked in from the start, and finding thoughtful ways to bring these models into the actual workflow of clinical decision-making. It's not just about having the tech, it's about making it usable in the places that matter most.