Abstract
Scaling in random time series is a novel way of gaining insight into mechanisms of complex systems. Scaling parameters characterize searches for the presence of "essential" uncertainty in outcomes of complex systems. The presence of this type of uncertainty in the emergency system (ES) can point to unusual ways of relieving the financial burden to the health system and improve the public health. The objective of this paper is to test for the presence of "essential" uncertainty in an outcome variable of the ES, i.e. emergency-ward length of stay (EWLS). An inverse power law (IPL) function fit to data can assess "essential" uncertainty. The study of EWLS has been undertaken in ten large Quebec hospitals. We find that all hospital EWLS are well fit by an IPL, as determined by an aggregated allometric relation. The presence of "essential" uncertainty is further confirmed by stratifying the EWLS according to proxies of disease complexity and severity at entry in emergency ward. Results point out that the various hospital dynamic systems embed "essential" uncertainty to various degrees. We conclude that intervention to reduce hospital health care costs must be centered on the interaction and feedback characterizing the ES processing of the patients.
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