Circular Statistical Analysis of Emergency Department Admissions
DOI:
https://doi.org/10.6000/1929-6029.2026.15.05Keywords:
Circular Statistics, Emergency Department, Temporal Patterns, von Mises Distribution, Uncertainty Quantification, Healthcare OperationsAbstract
Health-care administrators face ongoing challenges managing emergency department (ED) operations, particularly in understanding how patient arrival trends fluctuate within the 24-hour day. Although prior research has examined the times at which patients seek emergency care, most of these studies have used simple statistical methods that do not account for time as a periodic variable. As a result, many significant time-of-day patterns may not be detected. We use circular statistics on 142,005 hourly emergency department admissions at a large hospital in Iowa from January 2014 to August 2017. Overall, the pattern of ED visits presents an anisotropic distribution that is statistically significant according to both Rayleigh and Kuiper tests. Patient arrival times show a circular mean in the early to mid-afternoon, a marked late-afternoon modal peak, and a diffuse distribution across the day. Adjusted circular probability models such as the von Mises distribution, cardioid distribution, and the wrapped normal distribution perform significantly better than the circular uniform model when the AIC, BIC, CAIC, and HQIC criteria are considered. The circular summary charts help in understanding the various trends observed in the time series graph. In pointing out how the use of a circular method is a mathematically appropriate and more interpretable approach for describing trends related to admissions on an hourly basis, this piece of research also points out the benefits of such a method as being a useful tool for health-care planners.
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