ABSTRACT
Study Objectives: As the fourth wave of coronavirus disease 2019 (COVID-19) surges in Michigan, most health care systems are experiencing an increased hospitalization rate of infected COVID-19 patients. Understanding the arrival rates of patients to the emergency department (ED) is fundamental in managing the limited health care resources. Our objective is to develop an accurate forecasting model based on ED patients’ arrival and COVID-19 status to help manage and facilitate a data-driven resource planning. Methods: A cohort study of patients with clinical suspicion of COVID-19 evaluated at 2 EDs within an integrated health system that cares for a racially diverse population. We included patient arrivals, COVID-19 status, and demographic information between the dates of January 1, 2020 and March 16, 2021. We developed deep learning models (Long Short-Term Memory (LSTM)) to forecast patient arrivals in two geographically diverse EDs (denoted as ED1 and ED2). We used data from January to December 2020 for model training and data from January 2021 to March 2021 for model validation. The models are evaluated based on the root mean squared error (RMSE), the square root of the average of the squared error between predicted and observed values, and the mean absolute error (MAE), which provides the mean absolute difference between the predicted and the observed ED patient arrival rates per day. Results: In ED1, there were 56, 61 total patient arrivals (1, 433 infected COVID-19 patients) with a mean age of 38.0 ± 21.2 years. A majority were female (33, 457, 59.1%) and 29, 040 (51.3%) were Black. The average patient arrival per day was 125.1 (SD 35.0) for those without COVID-19, and 3.3 (SD 3.6) for COVID-1 confirmed patients. In ED2, there were 74, 176 total patient arrivals (1, 546 infected COVID-19 patients) with a mean age of 45.0 ± 23.0 years. A majority were female (39, 521, 53.3%) and 10, 636 (14.3%) were Black. The average patient arrival per day was 164.7 (SD 33.2) for those without COVID-19, and 3.5 (SD 5.0) for COVID 19 confirmed patients. Figure 1 shows the observed and predicted patients’ arrival for the two EDs for regular and confirmed COVID-19 patients. The LSTM models show accurate prediction one week in advance of daily patient arrivals for ED1 and ED2 with RMSE scores of 17 and 20 patients, respectively. The MAE values imply that, on average, the forecast’s error from the true daily patient arrival rate is 13.9 and 16.0 for ED1 and ED2, respectively. For COVID-19 patient arrivals to ED1 and ED2, the RMSE score is 3 patients each, while th MAE values are 2.2 and 2.4, respectively. Conclusion: This study demonstrates that an average RMSE prediction score of 18.5 and 3 patient arrivals per day for regular and COVID-19 confirmed patients is possible across EDs using LSTM one week prior to forecasting. Future validation and implementation of such forecasting models could impact effective planning and allocation of limited ED and hospital resources. [Formula presented]