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Forecasting to support EMS tactical planning: what is important and what is not

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Mostafa Rezaei
Mostafa Rezaei
Mostafa Rezaei
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Armann Ingolfsson at University of Alberta
Armann Ingolfsson
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Abstract and Figures

Forecasting emergency medical service (EMS) call volumes is critical for resource allocation and planning. The development of many commercial and free software packages has made a variety of forecasting methods accessible. Practitioners, however, are left with little guidance on selecting the most appropriate method for their needs. Using 5 years of data from 3 cities in Alberta, we compute exponential smoothing and benchmark forecasts for 8-hour periods for each ambulance station catchment area and with a forecast horizon of two weeks—a spatio-temporal resolution appropriate for tactical planning. The methods that we consider differ on three spectra: the number and type of time-series components, whether forecasts are computed individually or jointly, and the way in which forecasts at a specific resolution are converted to forecasts at the resolution of interest. We find that it is important to include a weekly seasonal component when forecasting EMS demand. Multiplicative seasonality, however, shows no benefit over additive seasonality. Adding other time-series components (e.g., trend, ARMA errors, Box-Cox transformation) does not improve performance. Spatial resolutions of station catchment area and lower, and temporal resolution of 4–24 hours perform similarly. We adapt an existing hierarchical forecasting framework to a two-dimensional spatio-temporal hierarchy, but find that hierarchical reconciliation of forecasts does not improve performance at the forecast resolution of interest for tactical planning. Neither does jointly forecasting time series. We show that added complexity does not materially improve forecasting performance. The simple methods that we find perform well are easy to implement and interpret, making implementation in practice more likely. In a simulation study we alter the empirical weekly patterns and demonstrate how extreme differences between the weekly seasonality patterns of different regions cause hierarchically-reconciled bottom-up approaches to outperform top-down approaches.
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Health Care Management Science (2024) 27:604–630
https://doi.org/10.1007/s10729-024-09690-7
Forecasting to support EMS tactical planning: what is important
and what is not
Mostafa Rezaei1·Armann Ingolfsson2
Received: 11 July 2022 / Accepted: 17 September 2024 / Published online: 19 October 2024
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024
Abstract
Forecasting emergency medical service (EMS) call volumes is critical for resource allocation and planning. The development
of many commercial and free software packages has made a variety of forecasting methods accessible. Practitioners, however,
are left with little guidance on selecting the most appropriate method for their needs. Using 5 years of data from 3 cities in
Alberta, we compute exponential smoothing and benchmark forecasts for 8-hour periods for each ambulance station catchment
area and with a forecast horizon of two weeks—a spatio-temporal resolution appropriate for tactical planning. The methods
that we consider differ on three spectra: the number and type of time-series components, whether forecasts are computed
individually or jointly, and the way in which forecasts at a specific resolution are converted to forecasts at the resolution of
interest. We find that it is important to include a weekly seasonal component when forecasting EMS demand. Multiplicative
seasonality, however, shows no benefit over additive seasonality. Adding other time-series components (e.g., trend, ARMA
errors, Box-Cox transformation) does not improve performance. Spatial resolutions of station catchment area and lower,
and temporal resolution of 4–24 hours perform similarly. We adapt an existing hierarchical forecasting framework to a two-
dimensional spatio-temporal hierarchy, but find that hierarchical reconciliation of forecasts does not improve performance
at the forecast resolution of interest for tactical planning. Neither does jointly forecasting time series. We show that added
complexity does not materially improve forecasting performance. The simple methods that we find perform well are easy
to implement and interpret, making implementation in practice more likely. In a simulation study we alter the empirical
weekly patterns and demonstrate how extreme differences between the weekly seasonality patterns of different regions cause
hierarchically-reconciled bottom-up approaches to outperform top-down approaches.
Keywords Empirical research ·Health care management ·Service operations ·Emergency medical services ·Hierarchical
forecasting ·Operations research ·Operations management
Highlights
•We use exponential smoothing methods to forecast EMS
call volumes for 8-hour periods and station catchment
areas; a spatio-temporal resolution appropriate for tacti-
cal planning
BMostafa Rezaei
mrezaei@escp.eu
Armann Ingolfsson
aingolfs@ualberta.ca
1Information and Operations Management, ESCP Business
School, Paris, France
2Alberta School of Business, University of Alberta, Edmonton,
Canada
•We find that it is important to include a weekly seasonal
component, but adding other time-series components
(e.g., trend, ARMA errors, Box-Cox transformation)
does not improve performance. Neither does jointly fore-
casting time series.
•Simple top-down approaches that use constant propor-
tions to divide a daily forecast for an entire city into
8-hour periods and station catchments areas perform very
well.
1 Introduction
An effective healthcare system is crucial for ensuring the
wellbeing of citizens. Emergency medical services (EMS)
play a critical role as the first point of entry into the system
123
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