Healthcare systems were globally shocked by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the resulting disease, coronavirus disease 2019 (COVID-19). 1–3 On March 11, 2020, COVID-19 was declared a global pandemic by the World Health Organization (WHO).4 By mid-March, transmission of COVID-19 had rapidly accelerated, increasing case counts throughout the United States, and it was found that many patients with severe disease also had common comorbidities such as hypertension, obesity and diabetes .5 6 In the state of Delaware, the first presumptive positive case of COVID-19 was reported by the Delaware Division of Public Health on March 11, 2020.7 In order to mitigate the spread of the virus, the Governor of Delaware declared a state of emergency on March 13, 2020. The weeks that followed included several modifications to the original state of emergency to minimize the spread of the virus.
In response to the growing pandemic, ChristianaCare Health Services, Inc. (ChristianaCare), which serves the majority catchment area of Northern Delaware and the most populous county in the state, followed suit with its own measures to mitigate spread, postponing all elective procedures in hospitals and all ambulatory practices effective March 17, 2020 to adhere to state and CDC guidelines. The ambulatory services at ChristianaCare adjusted the delivery of healthcare services by reducing the number of in-person visits to minimize the risk to patients and healthcare providers, redirecting patients to telehealth when appropriate. This resulted in a decrease of patient volumes throughout the ambulatory system. With the uncertainty that COVID-19 presented then, the Phase 1 reopening that occurred on June 1, 2020, and the exponential rise in cases occurring now, it is essential to understand how the ambulatory setting will continue to be affected in order to develop proper guidelines.
To understand the impact of the novel virus, scientists rely on community spread models to predict possible transmission. The popular susceptible, infected, and recovered (SIR) epidemiologic model and variations of this model have been used to gauge community spread of a variety of infectious diseases such as influenza and dengue fever.8–12 SIR models have also been applied to inpatient settings to predict hospital capacity regarding admissions, ICU beds, and ventilators.9,11,13,14 In addition to SIR models, the current literature on predicting patient volume varies from descriptive statistics to advanced time series models, with most of the studies that have used time series forecasting models focusing on emergency department and hospital admissions.
Time series forecasting in ambulatory visits prior to the COVID19 pandemic have been described in a few reports.15–22 The most used method for time series forecasting is the Box-Jenkins method otherwise known as the AutoRegressive Integrated Moving Average (ARIMA) model.23 The ARIMA model has been used for its simplicity and flexibility in capturing linear patterns in a time series.17,19−22
The future implications of COVID-19 in healthcare are still unknown, specifically how it will continue to affect the ambulatory landscape. This work aims to inform COVID-19 and nonCOVID-19 ambulatory resources allocation as well as guide ambulatory practices reopening for in-person visits as in-person care might have been delayed. We propose an ARIMA time series model to capture the changes in ambulatory patient volumes as a result of COVID-19.
The primary objective of this study is to accurately forecast the number of COVID-19 and nonCOVID-19 weekly visits in primary care practices. The ability to forecast patient volumes in primary care locations by accurately evaluating the dynamic changes in patient visits and fitting these data to a statistical model is useful for the appropriate allocation of human and material resources for future planning. With the uncertainty that COVID-19 presents, healthcare systems have been adapting their ambulatory practices to adhere to state guidelines and prepare for state reopening phases. Therefore, we developed a time series model that provides an 8-week future forecast for ambulatory practices and compared it to a naïve moving average approach.