A Temporal Fusion Transformer for Long-term Explainable Prediction of Emergency Department Overcrowding

TL;DR

This paper introduces a novel deep learning model, the Temporal Fusion Transformer, for long-term, explainable prediction of emergency department overcrowding, achieving high accuracy in forecasting patient volume over four weeks.

Contribution

The paper presents a new deep learning architecture that effectively forecasts ED patient volume using calendar and time-series data, outperforming existing models.

Findings

Achieved a MAPE of 5.90% in Portugal's HRA

Forecasted patient volume with an RMSE of 84.41 people/day

Demonstrated the effectiveness of multivariate static and time-series covariates

Abstract

Emergency Departments (EDs) are a fundamental element of the Portuguese National Health Service, serving as an entry point for users with diverse and very serious medical problems. Due to the inherent characteristics of the ED; forecasting the number of patients using the services is particularly challenging. And a mismatch between the affluence and the number of medical professionals can lead to a decrease in the quality of the services provided and create problems that have repercussions for the entire hospital, with the requisition of health care workers from other departments and the postponement of surgeries. ED overcrowding is driven, in part, by non-urgent patients, that resort to emergency services despite not having a medical emergency and which represent almost half of the total number of daily patients. This paper describes a novel deep learning architecture, the Temporal Fusion Transformer, that uses calendar and time-series covariates to forecast prediction intervals and point predictions for a 4 week period. We have concluded that patient volume can be forecasted with a Mean Absolute Percentage Error (MAPE) of 5.90% for Portugal's Health Regional Areas (HRA) and a Root Mean Squared Error (RMSE) of 84.4102 people/day. The paper shows empirical evidence supporting the use of a multivariate approach with static and time-series covariates while surpassing other models commonly found in the literature.