A multi-granular stacked regression for forecasting long-term demand in Emergency Departments
Abstract 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 commission...
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| Format: | Article |
| Language: | English |
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BMC
2023-02-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02109-3 |
| _version_ | 1811165813991800832 |
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| author | Charlotte James Richard Wood Rachel Denholm |
| author_facet | Charlotte James Richard Wood Rachel Denholm |
| author_sort | Charlotte James |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-10T15:43:47Z |
| format | Article |
| id | doaj.art-072b1abe7f22435c843a5138a5fb9f39 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-10T15:43:47Z |
| publishDate | 2023-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-072b1abe7f22435c843a5138a5fb9f392023-02-12T12:14:06ZengBMCBMC Medical Informatics and Decision Making1472-69472023-02-0123111110.1186/s12911-023-02109-3A multi-granular stacked regression for forecasting long-term demand in Emergency DepartmentsCharlotte James0Richard Wood1Rachel Denholm2NIHR Bristol Biomedical Research Centre (BRC), University Hospitals Bristol and Weston NHS Foundation Trust and University of BristolModelling and Analytics, National Health Service (BNSSG ICB)NIHR Bristol Biomedical Research Centre (BRC), University Hospitals Bristol and Weston NHS Foundation Trust and University of BristolAbstract 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.https://doi.org/10.1186/s12911-023-02109-3Machine learningEmergency DepartmentPopulation HealthService demandForecasting |
| spellingShingle | Charlotte James Richard Wood Rachel Denholm A multi-granular stacked regression for forecasting long-term demand in Emergency Departments BMC Medical Informatics and Decision Making Machine learning Emergency Department Population Health Service demand Forecasting |
| title | A multi-granular stacked regression for forecasting long-term demand in Emergency Departments |
| title_full | A multi-granular stacked regression for forecasting long-term demand in Emergency Departments |
| title_fullStr | A multi-granular stacked regression for forecasting long-term demand in Emergency Departments |
| title_full_unstemmed | A multi-granular stacked regression for forecasting long-term demand in Emergency Departments |
| title_short | A multi-granular stacked regression for forecasting long-term demand in Emergency Departments |
| title_sort | multi granular stacked regression for forecasting long term demand in emergency departments |
| topic | Machine learning Emergency Department Population Health Service demand Forecasting |
| url | https://doi.org/10.1186/s12911-023-02109-3 |
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