An intelligent digital twin framework with AI-driven optimization for patient flow and clinical scheduling in smart healthcare systems - Takeaways - 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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  • 1

    The study proposes a multi-level AI-enhanced digital twin framework for analyzing patient flow and clinical scheduling in hospitals.

  • 2

    The framework includes temporal forecasting, clinical robustness, and outcome grounding to model hospital dynamics using real-life datasets.

  • 3

    An LSTM-based model demonstrates competitive prediction performance with R² = 0.6785, MAE = 0.0895, and RMSE = 0.1103.

  • 4

    Congestion patterns reveal maximum congestion occurs between 11:00 and 14:00, primarily due to moderate-acuity patients (ESI-3).

  • 5

    The framework allows for decision analysis through simulations, aiding in understanding patient interactions and resource management.

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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