No more winter crisis? Forecasting daily bed requirements for emergency department admissions to hospital

Towards reliable forecasting of healthcare capacity needs: A scoping review and evidence mapping

BACKGROUND

The COVID-19 pandemic has highlighted the critical importance of robust healthcare capacity planning and preparedness for emerging crises. However, healthcare systems must also adapt to more gradual temporal changes in disease prevalence and demographic composition over time. To support proactive healthcare planning, statistical capacity forecasting models can provide valuable information to healthcare planners. This systematic literature review and evidence mapping aims to identify and describe studies that have used statistical forecasting models to estimate healthcare capacity needs within hospital settings.

METHOD

Studies were identified in the databases MEDLINE and Embase and screened for relevance before items were defined and extracted within the following categories: forecast methodology, measure of capacity, forecast horizon, healthcare setting, target diagnosis, validation methods, and implementation.

RESULTS

84 studies were selected, all focusing on various capacity outcomes, including number of hospital beds/ patients, staffing, and length of stay. The selected studies employed different analytical models grouped in six items; discrete event simulation (N = 13, 15 %), generalized linear models (N = 21, 25 %), rate multiplication (N = 15, 18 %), compartmental models (N = 14, 17 %), time series analysis (N = 22, 26 %), and machine learning not otherwise categorizable (N = 12, 14 %). The review further provides insights into disease areas with infectious diseases (N = 24, 29 %) and cancer (N = 12, 14 %) being predominant, though several studies forecasted healthcare capacity needs in general (N = 24, 29 %). Only about half of the models were validated using either temporal validation (N = 39, 46 %), cross-validation (N = 2, 2 %) or/and geographical validation (N = 4, 5 %).

CONCLUSION

The forecasting models' applicability can serve as a resource for healthcare stakeholders involved in designing future healthcare capacity estimation. The lack of routine performance validation of the used algorithms is concerning. There is very little information on implementation and follow-up validation of capacity planning models.

Efficient algorithms for real-time syndromic surveillance

OBJECTIVE

Outbreaks of influenza-like diseases often cause spikes in the demand for hospital beds. Early detection of these outbreaks can enable improved management of hospital resources. The objective of this study was to test whether surveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between emergency department (ED) presentations with influenza-like illnesses provide efficient early detection of these outbreaks.

METHODS

Our study used data on ED presentations to major public hospitals in Queensland, Australia across 2017-2020. We developed surveillance algorithms for each hospital that flag potential outbreaks when the average time between successive ED presentations with influenza-like illnesses becomes anomalously small. We designed one set of algorithms to be responsive to a wide range of anomalous decreases in the time between presentations. These algorithms concurrently monitor three exponentially weighted moving averages (EWMAs) of the time between presentations and flag an outbreak when at least one EWMA falls below its control limit. We designed another set of algorithms to be highly responsive to narrower ranges of anomalous decreases in the time between presentations. These algorithms monitor one EWMA of the time between presentations and flag an outbreak when the EWMA falls below its control limit. Our algorithms use dynamic control limits to reflect that the average time between presentations depends on the time of year, time of day, and day of the week.

RESULTS

We compared the performance of the algorithms in detecting the start of two epidemic events at the hospital-level: the 2019 seasonal influenza outbreak and the early-2020 COVID-19 outbreak. The algorithm that concurrently monitors three EWMAs provided significantly earlier detection of these outbreaks than the algorithms that monitor one EWMA.

CONCLUSION

Surveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between ED presentations are highly efficient at detecting outbreaks of influenza-like diseases at the hospital level.

Time-series cohort study to forecast emergency department visits in the city of Milan and predict high demand: a 2-day warning system

OBJECTIVES

The emergency department (ED) is one of the most critical areas in any hospital. Recently, many countries have seen a rise in the number of ED visits, with an increase in length of stay and a detrimental effect on quality of care. Being able to forecast future demands would be a valuable support for hospitals to prevent high demand, particularly in a system with limited resources where use of ED services for non-urgent visits is an important issue.

DESIGN

Time-series cohort study.

SETTING

We collected all ED visits between January 2014 and December 2019 in the five larger hospitals in Milan. To predict daily volumes, we used a regression model with autoregressive integrated moving average errors. Predictors included were day of the week and year-round seasonality, meteorological and environmental variables, information on influenza epidemics and festivities. Accuracy of prediction was evaluated with the mean absolute percentage error (MAPE).

PRIMARY OUTCOME MEASURES

Daily all-cause EDs visits.

RESULTS

In the study period, we observed 2 223 479 visits. ED visits were most likely to occur on weekends for children and on Mondays for adults and seniors. Results confirmed the role of meteorological and environmental variables and the presence of day of the week and year-round seasonality effects. We found high correlation between observed and predicted values with a MAPE globally smaller than 8.1%.

CONCLUSIONS

Results were used to establish an ED warning system based on past observations and indicators of high demand. This is important in any health system that regularly faces scarcity of resources, and it is crucial in a system where use of ED services for non-urgent visits is still high.

Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis

Objective: Given the ever-changing flow of obstetric patients in the hospital, how the government and hospital management plan and allocate medical resources has become an important problem that needs to be urgently solved. In this study a prediction method for calculating the monthly and daily flow of patients based on time series is proposed to provide decision support for government and hospital management.

Methods: The historical patient flow data from the Department of Obstetrics and Gynecology of the First Hospital of Jilin University, China, from January 1, 2018, to February 29, 2020, were used as the training set. Seven models such as XGBoost, SVM, RF, and NNAR were used to predict the daily patient flow in the next 14 days. The HoltWinters model is then used to predict the monthly flow of patients over the next year.

Results: The results of this analysis and prediction model showed that the obstetric inpatient flow was not a purely random process, and that patient flow was not only accompanied by the random patient flow but also showed a trend change and seasonal change rule. ACF,PACF,Ljung_box, and residual histogram were then used to verify the accuracy of the prediction model, and the results show that the Holtwiners model was optimal. R2, MAPE, and other indicators were used to measure the accuracy of the 14 day prediction model, and the results showed that HoltWinters and STL prediction models achieved high accuracy.

Conclusion: In this paper, the time series model was used to analyze the trend and seasonal changes of obstetric patient flow and predict the patient flow in the next 14 days and 12 months. On this basis, combined with the trend and seasonal changes of obstetric patient flow, a more reasonable and fair horizontal allocation scheme of medical resources is proposed, combined with the prediction of patient flow.

Effects of weather, air pollution and Oktoberfest on ambulance-transported emergency department admissions in Munich, Germany

BACKGROUND

Climate change and increasing risks of extreme weather events affect human health and lead to changes in the emergency department (ED) admissions and the emergency medical services (EMS) operations. For a better allocation of resources in the healthcare system, it is essential to predict ED numbers based on environmental variables. This publication aims to quantify weather, air pollution and calendar-related effects on daily ED admissions.

METHODS

Analyses were based on 575,725 admissions from the web-based IVENA system recording all patients in the greater Munich area with pre-hospital emergency care in ambulance operations during 2014-2018. Linear models were used to identify statistically significant associations between daily ED admissions and calendar, meteorological and pollution factors, allowing for lag effects of one to three days. Separate analyses were performed for seasons, with additional subset analyses by sex, age and surgical versus internal department.

RESULTS

ED admissions were exceptionally high during the three-week Oktoberfest, particularly for males and on the weekends, as well as during the New Year holiday. Admissions significantly increased during the years of study, decreased in spring and summer holidays, and were lower on Sundays while higher on Mondays. In the warmer seasons, admissions were significantly associated with higher temperature, adjusting for the effects of sunshine and humidity in all age groups except for the elderly. Adverse weather conditions in non-summer seasons were either linked to increasing ED admissions (from storms, gust) or decreasing them from rain. Mostly, but not exclusively, in winter, increasing ED admissions were associated with colder minimum temperatures as well as with higher NO and PM₁₀ concentrations.

CONCLUSIONS

In addition to standard calendar-related factors, incorporating seasonal weather, air pollutant and interactions with patient demographics into resource planning models can improve the daily allocation of resources and staff of EMS operations at hospital and city levels.

Can syndromic surveillance help forecast winter hospital bed pressures in England?

Background

Health care planners need to predict demand for hospital beds to avoid deterioration in health care. Seasonal demand can be affected by respiratory illnesses which in England are monitored using syndromic surveillance systems. Therefore, we investigated the relationship between syndromic data and daily emergency hospital admissions.

Methods

We compared the timing of peaks in syndromic respiratory indicators and emergency hospital admissions, between 2013 and 2018. Furthermore, we created forecasts for daily admissions and investigated their accuracy when real-time syndromic data were included.

Results

We found that syndromic indicators were sensitive to changes in the timing of peaks in seasonal disease, especially influenza. However, each year, peak demand for hospital beds occurred on either 29^th or 30^th December, irrespective of the timing of syndromic peaks. Most forecast models using syndromic indicators explained over 70% of the seasonal variation in admissions (adjusted R square value). Forecast errors were reduced when syndromic data were included. For example, peak admissions for December 2014 and 2017 were underestimated when syndromic data were not used in models.

Conclusion

Due to the lack of variability in the timing of the highest seasonal peak in hospital admissions, syndromic surveillance data do not provide additional early warning of timing. However, during atypical seasons syndromic data did improve the accuracy of forecast intensity.