An intelligent digital twin framework with AI-driven optimization for patient flow and clinical scheduling in smart healthcare systems - Report - MDSpire

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

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Clinical Report: AI-Enhanced Digital Twin Model for Patient Flow Optimization

Overview

This study presents a multi-level AI-enhanced digital twin framework aimed at optimizing patient flow and clinical scheduling in emergency departments. Utilizing real-life datasets, the framework demonstrates insights into patient congestion patterns and waiting times.

Background

Emergency departments face numerous operational challenges, including unpredictable patient flow and resource limitations, which can lead to increased waiting times and clinician workload. Traditional scheduling methods often fall short in dynamic environments.

Data Highlights

MetricValue
R² (Prediction Performance)0.6785
Mean Absolute Error (MAE)0.0895
Root Mean Square Error (RMSE)0.1103
Mean Waiting Time35 min

Key Findings

  • The AI-enhanced digital twin framework incorporates three levels: temporal forecasting, clinical robustness, and outcome grounding.
  • Temporal intelligence level utilizes an LSTM-based model for patient flow prediction.
  • Maximum congestion in emergency departments occurs between 11:00 and 14:00, primarily due to moderate-acuity patients (ESI-3).
  • A negative association exists between waiting time and patient satisfaction.
  • Trend analysis indicates consistent temporal trends across datasets, reflecting hospital dynamics.

Clinical Implications

The findings suggest the use of AI-driven models in patient flow management and scheduling in emergency departments.

Conclusion

The study discusses the application of AI and digital twin technologies in addressing operational challenges in healthcare.

Related Resources & Content

  1. Frontiers in Digital Health, 2026 -- Exploring an AI-driven dynamic triage system for real-time patient risk reassessment in emergency departments in low-resource settings
  2. Journal of Medical Internet Research (JMIR), 2026 -- Sequencing AI Automation and Data Interoperability in Oncology Using a Scenario-Planning Framework Coupled With Discrete-Event Simulation: Proof-of-Concept Study
  3. ASCO AI in Oncology, 2026 -- Digital Twins in Oncology: From Concept to Implementation
  4. Optometric Management, 2025 -- The Power of Efficient Patient Scheduling
  5. AHRQ Summit to Address Emergency Department Boarding, 2025
  6. Digital twins in healthcare: A systematic review of current applications, frameworks, and future directions - PMC
  7. Machine learning for risk stratification in the emergency department (MARS-ED): a randomized controlled trial | Nature Communications
  8. AHRQ Summit to Address Emergency Department Boarding
  9. Digital twins in healthcare: A systematic review of current applications, frameworks, and future directions - PMC
  10. Machine learning for risk stratification in the emergency department (MARS-ED): a randomized controlled trial | Nature Communications

Original Source(s)

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

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