Presentations in this panel present strategies for identifying and addressing health disparities in the early detection of ASD, with a focus ranging from evaluating psychometric properties of specific screening tools (BITSEA, M-CHAT) in diverse populations to qualitative and systems-level visual analysis of multiple aspects of service delivery in community-based settings. The use of methods such as machine learning (M-CHAT) and receiver operating characteristic analyses (BITSEA) to optimize screener effectiveness highlights the availability of psychometrically-sound screening instruments for use in diverse populations. However, broader systems analyses of completing multi-stage screening, obtaining referrals for diagnostic evaluations, participating in diagnostic evaluations, and receiving services reveal challenges to implementation of ASD screening in community-based settings. Challenges include limited capacity of the service delivery system, family factors such as hesitance to acknowledge a child’s problem, and finances. Implementation of ASD screening is best understood as a dynamic process that is shaped by organizational setting and mission and the unique characteristics of the populations being served. Therefore, it is important to document variations in practices designed to reach racial and ethnic minorities. Multiple contextual factors must be considered and multiple stakeholders engaged when supporting families attempting to navigate service delivery systems for children with possible ASD.