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Related Experiment Videos

Predicting the Unpredictable: A Data-Driven Machine Learning Model for Emergency Department Waiting Room Surge

Derick Jones1, Laura Walker1, Lindsey Asher2

  • 1Department of Emergency Medicine, Mayo Clinic, Rochester, MN.

Mayo Clinic Proceedings. Digital Health
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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Machine learning accurately predicts emergency department (ED) waiting room surge status up to 12 hours in advance. This AI tool helps hospitals manage patient flow and resources effectively.

Area of Science:

  • Artificial Intelligence in Healthcare
  • Machine Learning for Operations Management
  • Emergency Medicine Analytics

Background:

  • Emergency departments (EDs) face challenges managing fluctuating patient volumes.
  • Predicting patient surges is crucial for optimizing resource allocation and patient care.
  • Existing methods for surge prediction often lack accuracy over extended forecast horizons.

Purpose of the Study:

  • To develop and validate a machine learning algorithm for predicting ED waiting room surge status.
  • To forecast surge categories (green, yellow, red) 4, 8, and 12 hours in advance.
  • To assess the predictive performance of deep neural networks and XGBoost models.

Main Methods:

  • Retrospective cohort study with prospective validation (July 2023 - April 2025).

Related Experiment Videos

  • Analysis of 154,956 ED encounters using operational metrics and timestamps with 72-hour lagging data.
  • Training of deep neural network and gradient boosted decision tree (XGBoost) models to predict surge categories.

    • Main Results:

    • XGBoost model demonstrated strong predictive performance across all forecast horizons (up to 12 hours).
    • Area Under the Curve (AUC) for green/yellow levels ranged from 0.87-0.91; red levels showed AUCs of 0.76-0.77.
    • Operational accuracy of 68%-70% achieved for multiclass forecasting, indicating real-world applicability.

    Conclusions:

    • XGBoost models effectively differentiate ED surge states using operational data and timestamps.
    • Accurate forecasting of high-volume risk enables proactive operational adjustments.
    • This predictive capability can significantly improve ED workflow and patient management.