Accurately forecasting patient arrivals at Urgent Care Clinics (UCCs) and Emergency Departments (EDs) is important for effective resourcing and patient care. However, correctly estimating patient flows is not straightforward since it depends on many drivers. The predictability of patient arrivals has recently been further complicated by the COVID-19 pandemic conditions and the resulting lockdowns. This study investigates how a suite of novel quasi-real-time variables like Google search terms, pedestrian traffic, the prevailing incidence levels of influenza, as well as the COVID-19 Alert Level indicators can both generally improve the forecasting models of patient flows and effectively adapt the models to the unfolding disruptions of pandemic conditions. This research also uniquely contributes to the body of work in this domain by employing tools from the eXplainable AI field to investigate more deeply the internal mechanics of the models than has previously been done. The Voting ensemble-based method combining machine learning and statistical techniques was the most reliable in our experiments. Our study showed that the prevailing COVID-19 Alert Level feature together with Google search terms and pedestrian traffic were effective at producing generalisable forecasts. The implications of this study are that proxy variables can effectively augment standard autoregressive features to ensure accurate forecasting of patient flows. The experiments showed that the proposed features are potentially effective model inputs for preserving forecast accuracies in the event of future pandemic outbreaks.

The past 10 years have given us some truly innovative technology; now, healthcare providers are beginning to figure out the best ways to use it. They would do well to follow other industries by listening to consumers – in this case, patients – to determine the best way to incorporate this technology into their workflows. In this guest post, Vinay Seth Mohta, a managing director at an artificial intelligence engineering services firm, offers three patient-focused AI applications that might be a good place for healthcare executives to start. Accenture's "2018 Consumer Survey on Digital Health" found that three-quarters of the patients surveyed use technology to manage their own health. In addition, patients said they were eager to incorporate a new kind of technology into their health care: artificial intelligence.