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Forecasting emergency department arrivals using INGARCH models.

Juan C Reboredo1,2, Jose Ramon Barba-Queiruga3, Javier Ojea-Ferreiro4

  • 1Department of Economics, University of Santiago (USC), Santiago de Compostela, Spain.

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Forecasting emergency department patient arrivals is crucial. Integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) models enhance arrival predictions, aiding in staff allocation and surge management.

Keywords:
Emergency departmentForecastingINGARCH modelsPatient arrivals

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Area of Science:

  • Healthcare Operations Research
  • Biostatistics
  • Time Series Analysis

Background:

  • Accurate forecasting of emergency department (ED) patient arrivals is vital for effective hospital management.
  • Predicting patient flow is essential for resource allocation and mitigating the impact of patient surges.

Purpose of the Study:

  • To evaluate the utility of historical patient arrival data for forecasting daily ED arrivals.
  • To assess if past mean values and observations improve prediction accuracy.

Main Methods:

  • Utilized an integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) model.
  • Incorporated past arrival data and analyzed arrival volatility dynamics.
  • Examined conditional distribution fit and forecasting performance.

Main Results:

  • INGARCH models demonstrated improved in-sample and out-of-sample forecast accuracy.
  • Forecast improvements were particularly notable at the lower and upper quantiles of arrival distributions.
  • The model effectively captures the dynamics of arrival volatility.

Conclusions:

  • INGARCH modeling offers a valuable tool for short-term, tactical emergency department planning.
  • This approach aids in optimizing staff rotas and resource deployment for unexpected patient influxes.
  • Enhances operational efficiency in emergency departments through improved forecasting.