Interweaving Met averse Governance and Predictive Health Analytics for Smarter Policy Making

Abstract

Bringing metaverse technologies into healthcare is changing the way patients and providers connect, how data flows between systems, and what policy needs to look like in increasingly immersive care environments. As predictive analytics take on a larger role in shaping public health decisions, there’s a growing need to examine how digital governance structures help, or sometimes hold back, the ability to turn predictions into policy that actually works on the ground. This study takes a closer look at where predictive health analytics and metaverse governance meet. The goal is to develop a framework that brings together spatial data systems, AI-powered prediction models, and real-time monitoring tools in a way that informs smarter, more responsive policy decisions. Using a mix of patient data drawn from both virtual and physical settings, the models used in this study, gradient boosting machines, recurrent neural networks, and ensemble classifiers, were trained to predict outcomes like hospital readmission and early indicators of chronic disease. We evaluated their performance using F1-score, ROC-AUC, and precision-recall to make sure the results held up under scrutiny. What we found was that predictive models embedded in well-governed virtual environments tend to drive faster and more targeted policy responses. This was especially true when spatial and wearable data were integrated under clear and accountable data governance protocols. In other words, when the rules of the digital space were clearly defined, the insights produced by the models could actually be used to guide real-time interventions. These findings suggest there's real value in designing predictive systems to fit within the regulatory and ethical structures of healthcare’s digital future. If we’re going to use AI tools to guide decisions in these immersive platforms, those tools need to be transparent, aligned with public interest, and built into systems that can act on the insights they generate. For policymakers and system architects, this means thinking carefully about how to align platform design with responsible AI use, and how to make room for predictive tools that don’t just monitor risk, but help shape better, more equitable health outcomes.