Data analysis
For each outcome, we applied two statistical modeling approaches. To test the overall implementation effect, we fitted a multivariable generalized linear mixed model (GLMM) for continuous outcomes or generalized linear model (GLM) for categorical outcomes, with a binary intervention term (‘pre’ vs. ‘post’ implementation) as the main predictor. To examine outcomes relative to implementation timing, we adopted an interrupted time-series approach using a ‘time’ indicator to represent the number of months before or after implementation, with segments spanning 3–4 months, and then fitted generalized least squares (GLS) for continuous outcomes or a GLM for binary or ordinal outcomes, using linear splines [Models included the following covariates of methodological or substantive importance while maintaining an approximate 10:1 observation-to-parameter ratio: site; diagnosis; triage level; distance to site; neighborhood socioeconomic status [