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Forecasting the Emergency Department Patients Flow.

Mohamed Afilal1, Farouk Yalaoui2, Frédéric Dugardin2

  • 1Institut Charles Delaunay, LOSI, Université de Technologie de Troyes UMR 6281, CNRS, 12 Rue Marie Curie, CS 42060, cedex, 10004, Troyes, France. mohamed.afilal@utt.fr.

Journal of Medical Systems
|June 9, 2016
PubMed
Summary

Forecasting daily emergency department (ED) patient flow is crucial for hospital resource management. This study developed new time-series models for accurate ED attendance prediction, achieving over 91% accuracy for annual forecasts.

Keywords:
Emergency department flowForecastingPatient classificationTime series

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Area of Science:

  • Healthcare Management
  • Operations Research
  • Public Health

Background:

  • Emergency departments (EDs) are primary hospital entry points, leading to frequent overcrowding.
  • Effective demand forecasting is essential for optimizing ED operations and patient service quality.

Purpose of the Study:

  • To develop and evaluate innovative time-series models for forecasting daily emergency department attendance.
  • To propose a new patient classification system for EDs, consolidating existing categories.

Main Methods:

  • Conducted a case study at the Troyes city hospital center's ED.
  • Developed and applied novel time-series models for patient flow forecasting.
  • Introduced a consolidated ED patient classification system.

Main Results:

  • The developed models demonstrated high performance, achieving up to 91.24% accuracy in annual total flow forecasts.
  • Models showed robustness, performing well even during epidemic periods.
  • The new patient classification system offers a practical approach for ED management.

Conclusions:

  • Accurate ED patient flow forecasting is vital for efficient resource allocation (staff and materials).
  • The proposed time-series models provide a reliable tool for ED demand prediction.
  • The study offers a practical framework for improving ED management and patient care through data-driven insights.