A multi-granular stacked regression for forecasting long-term demand in Emergency Departments

Charlotte James, Richard Wood, Rachel Denholm

Research output: Contribution to journalArticlepeer-review


Background: In the United Kingdom, Emergency Departments (EDs) are under significant pressure due to an ever-increasing number of attendances. Understanding how the capacity of other urgent care services and the health of a population may influence ED attendances is imperative for commissioners and policy makers to develop long-term strategies for reducing this pressure and improving quality and safety. Methods: We developed a novel multi-granular stacked regression (MGSR) model using publicly available data to predict future mean monthly ED attendances within Clinical Commissioning Group regions in England. The MGSR combines measures of population health and health service capacity in other related settings. We assessed model performance using the R-squared statistic, measuring variance explained, and the Mean Absolute Percentage Error (MAPE), measuring forecasting accuracy. We used the MGSR to forecast ED demand over a 4-year period under hypothetical scenarios where service capacity is increased, or population health is improved. Results: Measures of service capacity explain 41 ± 4% of the variance in monthly ED attendances and measures of population health explain 62 ± 22%. The MGSR leads to an overall improvement in performance, with an R-squared of 0.79 ± 0.02 and MAPE of 3% when forecasting mean monthly ED attendances per CCG. Using the MGSR to forecast long-term demand under different scenarios, we found improving population health would reduce peak ED attendances per CCG by approximately 1000 per month after 2 years. Conclusion: Combining models of population health and wider urgent care service capacity for predicting monthly ED attendances leads to an improved performance compared to each model individually. Policies designed to improve population health will reduce ED attendances and enhance quality and safety in the long-term.

Original languageEnglish
Article number29
JournalBMC Medical Informatics and Decision Making
Issue number1
Publication statusPublished - 7 Feb 2023

Bibliographical note

Funding Information:
CJ and RD are funded by NIHR Bristol BRC (BRC_1215_20011). CJ is funded by NIHR Research Capability Funding (RCF 21/22 − 4.2). RD is funded by HDR UK South West CFC0129.

Funding Information:
This study was supported by the National Institute for Health and Care Bristol Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Availability of data and materials
The data generated and analysed during the study are available on GitHub:


  • Emergency Department
  • Forecasting
  • Machine learning
  • Population Health
  • Service demand

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics
  • Computer Science Applications


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