Abstract
This commentary systematically explores how Data‑Driven Decision Support Systems (DSS) in pervasive healthcare reconcile innovation with privacy and sustainability. It highlights technical approaches—such as cross-source data fusion using enhanced Dempster–Shafer theory, federated learning, and homomorphic encryption—while analyzing specific case studies in ophthalmology (e.g., retinal disease detection models), MRI, and CT imaging that demonstrate real-world implementation strategies. The paper also reviews solutions to core issues like data cleaning, encryption optimization, algorithmic explainability, and ecological footprint mitigation. The commentary concludes that successful DSS adoption depends on integrative technical frameworks supported by cross-disciplinary collaboration and transparent governance.