A multi-method patient arrival forecasting outline for hospital emergency departments

Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning

BACKGROUND

Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of calendar and meteorological predictors, as well as feature-engineered variables, to predict daily patient arrivals using datasets from eleven different EDs across three countries.

METHODS

Six machine learning (ML) algorithms were tested on forecasting horizons of 7 and 45 days. Three of them - Light Gradient Boosting Machine (LightGBM), Support Vector Machine with Radial Basis Function (SVM-RBF), and Neural Network Autoregression (NNAR) - were never before reported for predicting ED patient arrivals. Algorithms' hyperparameters were tuned through a grid-search with cross-validation. Prediction performance was assessed using fivefold cross-validation and four performance metrics.

RESULTS

The eXtreme Gradient Boosting (XGBoost) was the best-performing model on both prediction horizons, also outperforming results reported in past studies on ED arrival prediction. XGBoost and NNAR achieved the best performance in nine out of the eleven analyzed datasets, with MAPE values ranging from 5.03% to 14.1%. Feature engineering (FE) improved the performance of the ML algorithms.

CONCLUSION

Accuracy in predicting ED arrivals, achieved through the FE approach, is key for managing human and material resources, as well as reducing patient waiting times and lengths of stay.

Predicting hospital emergency department visits accurately: A systematic review

OBJECTIVES

The emergency department (ED) is a very important healthcare entrance point, known for its challenging organisation and management due to demand unpredictability. An accurate forecast system of ED visits is crucial to the implementation of better management strategies that optimise resources utilization, reduce costs and improve public confidence. The aim of this review is to investigate the different factors that affect the ED visits forecasting outcomes, in particular the predictive variables and type of models applied.

METHODS

A systematic search was conducted in PubMed, Web of Science and Scopus. The review methodology followed the PRISMA statement guidelines.

RESULTS

Seven studies were selected, all exploring predictive models to forecast ED daily visits for general care. MAPE and RMAE were used to measure models' accuracy. All models displayed good accuracy, with errors below 10%.

CONCLUSIONS

Model selection and accuracy was found to be particularly sensitive to the ED dimension. While ARIMA-based and other linear models have good performance for short-time forecast, some machine learning methods proved to be more stable when forecasting multiple horizons. The inclusion of exogenous variables was found to be advantageous only in bigger EDs.

A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach

Demand forecasting has been a major concern of operational strategy to manage the inventory and optimize the customer satisfaction level. The researchers have proposed many conventional and advanced forecasting techniques, but no one leads to complete accuracy. Forecasting is equally important in manufacturing as well as retail companies. In this study, the performances of five regression techniques of machine learning, viz. random forest (RF), extreme gradient boosting (XGBoost), gradient boosting, adaptive boosting (AdaBoost), and artificial neural network (ANN) algorithms, are compared with a proposed hybrid (RF-XGBoost-LR) model for sales forecasting of a retail chain considering the various parameters of forecasting accuracy. The weekly sales data of a US-based retail company is considered in the analysis of the forecasts undertaking the attributes affecting the sale such as the temperature of the region and the size of the store. It is observed that the hybrid RF-XGBoost-LR outperformed the other models measured against various metrics of performance. This study may help the industry decision-maker to understand and improve the methods of forecasting.

Predicting Emergency Department Utilization among Older Hong Kong Population in Hot Season: A Machine Learning Approach

Previous evidence suggests that temperature is associated with the number of emergency department (ED) visits. A predictive system for ED visits, which takes local temperature into account, is therefore needed. This study aimed to compare the predictive performance of various machine learning methods with traditional statistical methods based on temperature variables and develop a daily ED attendance rate predictive model for Hong Kong. We analyzed ED utilization among Hong Kong older adults in May to September from 2000 to 2016. A total of 103 potential predictors were derived from 1- to 14-day lag of ED attendance rate and meteorological and air quality indicators and 0-day lag of holiday indicator and month and day of week indicators. LASSO regression was used to identify the most predictive temperature variables. Decision tree regressor, support vector machine (SVM) regressor, and random forest regressor were trained on the selected optimal predictor combination. Deep neural network (DNN) and gated recurrent unit (GRU) models were performed on the extended predictor combination for the previous 14-day horizon. Maximum ambient temperature was identified as a better predictor in its own value than as an indicator defined by the cutoff. GRU achieved the best predictive accuracy. Deep learning methods, especially the GRU model, outperformed conventional machine learning methods and traditional statistical methods.

NO2 exposure increases eczema outpatient visits in Guangzhou, China: an indication for hospital management

Background

Ambient nitrogen dioxide (NO₂) is a common air pollutant in developing countries and causes skin conditions, but its effect on eczema in subtropical areas is not clear in China.

Object

To measure the effect of short-term exposure of NO₂ on the incidence of eczema and the change of outpatient visits.

Methods

Data of daily temperature, air pollutants, and outpatient visits from 2013 to 2018 were collected in a row. The generalized additive model (GAM) and Poisson distribution were used to assess the association between short-term exposure of NO₂ and the outpatient visits of patients with eczema. The cumulative exposure effect of lag 0–3 days and the displacement effect of NO₂ and other pollutants were considered as well. A single pollutant model was used to examine the independent association, and a two-pollutant model was adopted to control the confounding effect.

Results

The daily outpatient visits of eczema increased from 75.26 to 190.85 from 2013 to 2018 (P < 0.001). The combined influence of NO₂ and the related pollutant exerted a stronger influence on the incidence of eczema. The maximum effect of NO₂ appeared on the exposed day.

(lag 0) and disappeared on day 4 (lag 3). The children and seniors were more vulnerable to NO₂ exposure.

Conclusion

Exposure to NO₂ is tightly associated with eczema incidence and outpatient visits. The hospitals should react to the visit fluctuations and adjust physician duty shifts to improve outpatient service efficiency.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10549-7.

Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study

BACKGROUND

The overcrowding of hospital outpatient and emergency departments (OEDs) due to chronic respiratory diseases in certain weather or under certain environmental pollution conditions results in the degradation in quality of medical care, and even limits its availability.

OBJECTIVE

To help OED managers to schedule medical resource allocation during times of excessive health care demands after short-term fluctuations in air pollution and weather, we employed machine learning (ML) methods to predict the peak OED arrivals of patients with chronic respiratory diseases.

METHODS

In this paper, we first identified 13,218 visits from patients with chronic respiratory diseases to OEDs in hospitals from January 1, 2016, to December 31, 2017. Then, we divided the data into three datasets: weather-based visits, air quality-based visits, and weather air quality-based visits. Finally, we developed ML methods to predict the peak event (peak demand days) of patients with chronic respiratory diseases (eg, asthma, respiratory infection, and chronic obstructive pulmonary disease) visiting OEDs on the three weather data and environmental pollution datasets in Guangzhou, China.

RESULTS

The adaptive boosting-based neural networks, tree bag, and random forest achieved the biggest receiver operating characteristic area under the curve, 0.698, 0.714, and 0.809, on the air quality dataset, the weather dataset, and weather air quality dataset, respectively. Overall, random forests reached the best classification prediction performance.

CONCLUSIONS

The proposed ML methods may act as a useful tool to adapt medical services in advance by predicting the peak of OED arrivals. Further, the developed ML methods are generic enough to cope with similar medical scenarios, provided that the data is available.

An exhaustive review and analysis on applications of statistical forecasting in hospital emergency departments

Emergency departments (EDs) provide medical treatment for a broad spectrum of illnesses and injuries to patients who arrive at all hours of the day. The quality and efficient delivery of health care in EDs are associated with a number of factors, such as patient overall length of stay (LOS) and admission, prompt ambulance diversion, quick and accurate triage, nurse and physician assessment, diagnostic and laboratory services, consultations and treatment. One of the most important ways to plan the healthcare delivery efficiently is to make forecasts of ED processes. The aim this study is thus to provide an exhaustive review for ED stakeholders interested in applying forecasting methods to their ED processes. A categorisation, analysis and interpretation of 102 papers is performed for review. This exhaustive review provides an insight for researchers and practitioners about forecasting in EDs in terms of showing current state and potential areas for future attempts.