The convergence of continuous physiological monitoring and intelligent building systems in smart clinics offers a transformative opportunity for patient-centered care, yet it introduces the challenge of harmonizing clinical fidelity, patient comfort, and operational sustainability. We present DT-ECO, a privacy-preserving digital twins framework that enables decision-centric co-management of multi-modal patient monitoring and clinical environmental systems. DT-ECO constructs a hybrid digital twin that integrates a physics-informed building model with graph-temporal physiological inference and battery electrochemistry, enabling real-time synchronization between patient state, IoT device operation, and environmental dynamics within a differentiable programming environment. On this foundation, a hierarchical control strategy is developed, in which a constrained deep reinforcement learning agent adaptively schedules wearable IoT sensor sampling to extend device lifetime, while a model predictive controller orchestrates HVAC operation and on-site energy resources to maintain a therapeutic environment. Extensive evaluations on DOE reference hospitals and public ECG datasets demonstrate that DT-ECO achieves a 31.8% reduction in annual energy consumption and extends median wearable battery life by 28%, while rigorously maintaining clinical standards-evidenced by less than 0.6% thermal comfort violation and no degradation in arrhythmia detection capability (F1-score 0.956). By bridging the gap between patient physiology and the clinical environment, DT-ECO establishes a pathway toward precision healthcare facilities that are simultaneously patient-centric, diagnostically robust, and operationally sustainable.
Liu et al. (Thu,) studied this question.