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Related Experiment Video

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

From model to forecasting: a multicenter study in emergency departments.

Mathias Wargon1, Enrique Casalino, Bertrand Guidet

  • 1Institut national de la santé et de la recherche médicale, Paris, France.

Academic Emergency Medicine : Official Journal of the Society for Academic Emergency Medicine
|September 15, 2010
PubMed
Summary

Mathematical models using calendar variables can predict emergency department (ED) census, especially when combining data from multiple hospitals. Forecasts for a virtual mega ED showed higher accuracy than individual hospital predictions.

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Published on: May 15, 2020

Area of Science:

  • Emergency Medicine
  • Health Services Research
  • Mathematical Modeling

Background:

  • Emergency departments (EDs) face challenges in managing patient flow and resource allocation due to fluctuating patient volumes.
  • Predictive modeling using historical data can aid in anticipating ED census and optimizing operational efficiency.

Purpose of the Study:

  • To investigate the utility of mathematical models with calendar variables in identifying determinants of ED census.
  • To assess the performance of long-term ED attendance forecasts.
  • To compare model accuracy for individual EDs versus a combined virtual ED.

Main Methods:

  • A general linear model (GLM) utilizing calendar variables was applied to daily ED visit data from four academic hospitals in Paris (2004-2007).
  • Models were developed and tested on two consecutive 2-year periods, and forecasts for 2007 were generated using data from 2004-2006.
  • Model and forecast accuracy were evaluated using the mean absolute percentage error (MAPE) for individual EDs and a virtual aggregated ED.

Main Results:

  • Models explained up to 50% of visit variations with a MAPE below 10%.
  • Daily visit patterns varied significantly between individual EDs, showing no clear seasonality.
  • Forecasts for the combined virtual ED achieved a MAPE of 5.3%, while individual ED forecasts ranged from 8.1% to 17.0%.

Conclusions:

  • Calendar-based determinants of ED attendance differed significantly even among geographically close hospitals over short time frames.
  • Mathematical models and forecasts demonstrate greater value for predicting combined ED attendance across multiple facilities.
  • These models offer a valuable tool for anticipating staffing needs and optimizing resource allocation in settings with shared healthcare facilities.