An intelligent digital twin framework with AI-driven optimization for patient flow and clinical scheduling in smart healthcare systems
By
Stalin Victor Balthasar
Suguna Marappan
Logesh Ravi
July 15, 2026
Clinical Scorecard: A Multi-Level AI-Enhanced Digital Twin Model for Optimizing Patient Flow and Clinical Scheduling in Advanced Healthcare Systems
At a Glance
Category Detail
Condition Patient Flow and Clinical Scheduling Optimization Key Mechanisms AI-enhanced digital twin framework incorporating temporal forecasting, clinical robustness, and outcome grounding. Target Population Patients in emergency departments. Care Setting Advanced healthcare systems.
Key Highlights
Proposes a multi-level AI-enhanced digital twin framework for hospital management. Incorporates LSTM-based models for temporal forecasting with competitive prediction performance. Identifies peak congestion times in emergency departments and their causes. Demonstrates a negative association between waiting time and patient satisfaction. Enables decision analysis through simulations without disrupting patient services. Guideline-Based Recommendations
Diagnosis
Management
Utilize AI-powered digital twin systems for modeling hospital operations. Monitoring & Follow-up
Risks
Patient & Prescribing Data
Patients in emergency departments experiencing variable demand and resource constraints.
AI predictions can enhance scheduling and resource allocation.
Clinical Best Practices
Implement digital twin technology to analyze patient flow and scheduling. Use predictive analytics to forecast patient arrivals and treatment times. Related Resources & Content
