Statistical Analyses. All analyses will be conducted at the individual patient level using a generalized linear mixed model (GLMM). Separate GLMMs will be used for outcome. GLMMs account for the stepped wedge design, individual level covariates, and service level covariates, and allow each service to have different number of patients at each time point. For all secondary outcomes, the investigators will investigate the best fitting distributional form (i.e., Normal, gamma) to estimate relative risks (RRs) for the intervention effect. Each model will include treatment group (intervention/control), time (5 planned periods), season, age, ethnicity, insurance status, baseline patient risk, time of hospital admission (day on weekdays: 8 am - 5 pm; weekday night; weekend) as covariates, and a random effect for child (to account for within subject correlation due to possible repeat admissions by the same child). Estimates of RRs and 95% credible intervals will be reported with posterior probabilities of benefit and cost-effectiveness. Prior distributions for all covariates will be neutral but excluding implausible large treatment effects ~N(0, SD=0.35).