Synergistic patient factors are driving recent increased pediatric urgent care demand

Objectives: We aimed to use the high fidelity urgent care patient data to model the factors that have led to the increased demand at our local pediatric urgent care centre. Methods: The dataset for this retrospective cohort study was obtained from our local healthcare centre’s national reporting data for pediatric urgent care visits from 2006 to 2022. Variables analyzed included: basic patient demographics, chief complaint, triage urgency, date and time of registration/discharge, discharge diagnosis, and discharge destination. Statistical analysis of non-linear trends was summarized by locally estimated scatterplot smoothing splines. For machine learning, we used the tidymodels R package. Models were validated in training using k-fold cross validation with k = 5. We used univariate linear regression as a baseline model. After the data was standardized, correlation and homoscedasticity were evaluated between all parameter permutations. Results: This dataset consisted of 164,660 unique visits to our academic centre’s pediatric urgent care. Over the study period, there was an overall substantial increase in the number of urgent care visits per day, with a rapid increase beyond previous levels in 2021 and further in 2022. The increased length of stay trend was consistent across presenting complaint categories. The proportion of patients without primary care in 2022 was 2.5 times higher than in 2013. A random forest machine learning model revealed the relative importance of features to predicting a visit in 2022 were: longer stay, later registration in the day, diagnosis of an infectious illness, and younger age. Conclusions: This study identified a combination of declining primary care access, circulating viral infections, and shifting chief complaints as factors driving the recent increase in frequency and duration of visits to our urgent care service. Author summary: This study leverages a large dataset of pediatric urgent care visits to demonstrate the complex interplay of factors driving the recent increased care burden. The patterns in the data are not always readily evident with standard statistical techniques, but using machine learning helps discern differences not previously evident. A combination of declining primary care access, circulating viral infections, and more urgent complex presentations appear to be driving the recent increase in frequency and duration of visits to our urgent care service. These increases began prior to the COVID-19 pandemic in 2020, but appear to have been exacerbated by this event.