A unified machine learning approach to time series forecasting applied to demand at emergency departments.

Journal: BMC emergency medicine
Published Date: Jan 18, 2021

Abstract

BACKGROUND: There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provide adequate quality of care while maintaining standards and productivity. Managing hospital demand effectively requires an adequate knowledge of the future rate of admission. We develop a novel predictive framework to understand the temporal dynamics of hospital demand.

Authors

  • Michaela A C Vollmer
    MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK. m.vollmer@imperial.ac.uk.
  • Ben Glampson
    Imperial College Healthcare NHS Trust, London, UK.
  • Thomas Mellan
    MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
  • Swapnil Mishra
    MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
  • Luca Mercuri
    Imperial College Healthcare NHS Trust, London, UK.
  • Ceire Costello
    MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
  • Robert Klaber
    Imperial College Healthcare NHS Trust, London, UK.
  • Graham Cooke
    Imperial College Healthcare NHS Trust, London, UK.
  • Seth Flaxman
    Department of Mathematics and Data Science Institute, Imperial College London, London, SW7 2AZ, UK.
  • Samir Bhatt
    Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK.

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