Identifying seasonal and temporal trends in the pressures experienced by hospitals related to unscheduled care
Walker NJ., Van Woerden HC., Kiparoglou V., Yang Y.
© 2016 The Author(s). Background: As part of an electronic dashboard operated by Public Health Wales, senior managers at hospitals in Wales report daily "escalation" scores which reflect management opinion on the pressure a hospital is experiencing and ability to meet ongoing demand with respect to unscheduled care. An analysis was undertaken of escalation scores returned for 18 hospitals in Wales between the years 2006 and 2014 inclusive, with a view to identifying systematic temporal patterns in pressure experienced by hospitals in relation to unscheduled care. Methods: Exploratory data analysis indicated the presence of within-year cyclicity in average daily scores over all hospitals. In order to quantify this cyclicity, a Generalised Linear Mixed Model was fitted which incorporated a trigonometric function (sine and cosine) to capture within-year change in escalation. In addition, a 7-level categorical day of the week effect was fitted as well as a 3-level categorical Christmas holiday variable based on patterns observed in exploration of the raw data. Results: All of the main effects investigated were found to be statistically significant. Firstly, significant differences emerged in terms of overall pressure reported by individual hospitals. Furthermore, escalation scores were found to vary systematically within-year in a wave-like fashion for all hospitals (but not between hospitals) with the period of highest pressure consistently observed to occur in winter and lowest pressure in summer. In addition to this annual variation, pressure reported by hospitals was also found to be influenced by day of the week (low at weekends, high early in the working week) and especially low over the Christmas period but high immediately afterwards. Conclusions: Whilst unpredictable to a degree, quantifiable pressure experienced by hospitals can be anticipated according to models incorporating systematic temporal patterns. In the context of finite resources for healthcare services, these findings could optimise staffing schedules and inform resource utilisation.