Administrative Supplement: Rapid Actionable Data for Opioid Response in Kentucky (RADOR-KY)

Abstract The RADOR-KY project will build a robust state-wide surveillance system for opioid use disorder (OUD) including opioid overdose, integrating multiple data sources to monitor and predict drug overdose mortality and morbidity. This system will be used by stakeholders to inform data-driven action, supporting the coordination and targeting of prevention and treatment efforts. As proposed in the parent grant for this supplement, the RADOR-KY system will integrate several data sources, including Emergency Medical Services (EMS) data, to develop machine learning predictive models and forecasting for opioid overdoses to inform public health and public safety agencies'' actions and planning. The proposed administrative supplement of RADR-KY will improve our understanding on the ethical aspects of these machine learning/artificial intelligence methods. Emergency Medical Services run data for opioid overdose surveillance is a promising new system that overcomes limitations of traditional data sources, such as prolonged delays and omission of non-clinical overdose events. While recent national standards have improved the structural components of EMS encounter data, the quality and completeness of such data still necessitate reliance on patient care narratives for case assertion. There have been a host of opioid overdose definitions proposed, typically focus on keyword matches or other rule-based criteria, with little emphasis on definition validation, comparative evaluations, or demographic parity. Critically, No previous models, whether machine learning or rulebased, have considered the demographic fairness in their approaches. Leveraging our access to over 3.5 million EMS detailed encounter records access under RADOR-KY''s DUA, along with expert-labeled and extracted data, we aim to assess these proposed models against our own machine learning natural language processing classifier, particularly considering disparate populations.