| Abstract: This panel will explore the vital role of modern data pipelines in healthcare, using the National Autism Data Registry (NADR) as a case study to demonstrate how automation, machine learning (ML), and robust security practices empower providers to analyze and benchmark clinical data, driving data-informed decision-making. By accounting for social determinants of health, NADR ensures that insights reflect true care quality rather than external influences or diagnosis severity, helping providers enhance care while addressing disparities in outcomes. In collaboration with Juniper, the panel will showcase how NADR integrates tools designed to expand access to behavioral health care. Juniper’s solutions break down interoperability barriers, enabling clinicians to focus on delivering high-quality care without being overwhelmed by technical challenges. The panel will also highlight how NADR employs Johns Hopkins population health methodologies, combined with machine learning, to automate adjustments. This approach ensures that the insights generated focus on care quality, free from bias related to demographic variables, further promoting equitable care delivery. |