  Urgent care clinics and emergency departments around the world periodically
suffer from extended wait times beyond patient expectations due to inadequate
staffing levels. These delays have been linked with adverse clinical outcomes.
Previous research into forecasting demand this domain has mostly used a
collection of statistical techniques, with machine learning approaches only now
beginning to emerge in recent literature. The forecasting problem for this
domain is difficult and has also been complicated by the COVID-19 pandemic
which has introduced an additional complexity to this estimation due to typical
demand patterns being disrupted. This study explores the ability of machine
learning methods to generate accurate patient presentations at two large urgent
care clinics located in Auckland, New Zealand. A number of machine learning
algorithms were explored in order to determine the most effective technique for
this problem domain, with the task of making forecasts of daily patient demand
three months in advance. The study also performed an in-depth analysis into the
model behaviour in respect to the exploration of which features are most
effective at predicting demand and which features are capable of adaptation to
the volatility caused by the COVID-19 pandemic lockdowns. The results showed
that ensemble-based methods delivered the most accurate and consistent
solutions on average, generating improvements in the range of 23%-27% over the
existing in-house methods for estimating the daily demand.
