178: Evaluation of a Machine Learning-Powered Dashboard for Identification of Adverse Events Among Individuals with Intellectual and Developmental Disabilities

This poster will describe the various steps in an evaluation of a machine learning-powered dashboard designed to identify critical incidents among individuals with intellectual and developmental disabilities. By integrating case management data and medical claims data, the dashboard helps identify unreported adverse events, supporting proactive case management. Developed by technology company RSM for Hawaiʻi’s Developmental Disabilities Division and evaluated by the University of Hawaiʻi Social Science Research Institute, this dashboard represents an unprecedented cross-sector collaboration that leverages data to increase safety and improve outcomes of program participants. Attendees will learn about the dashboard’s interface and predictive analytics, along with evaluation tools, including model performance metrics, calibration testing, interviews, and surveys. Grounded in the Consolidated Framework for Implementation Research, the evaluation illuminated implementation facilitators and barriers, assisting with continuous dashboard enhancements. The poster will offer insights into the intersection of machine learning, data-driven decision-making, and implementation science.

Eva McKinsey – University of Hawaiʻi at Mānoa, Social Science Research Institute; Ashlyn Wong – University of Hawaiʻi at Mānoa, Social Science Research Institute; Charlie Iwata – University of Hawaiʻi at Mānoa, Social Science Research Institute; Meldrick Ravida – University of Hawaiʻi at Mānoa, Social Science Research Institute; Yone (Eric) Lin – University of Hawaiʻi at Mānoa, Social Science Research Institute; Pablo Orjales – RSM; Anh Dao – RSM; Robbie Beyer – RSM; Chelsea Tanimura – Hawaii Developmental Disabilities Division; George Casey – RSM; Ryan Lee – Hawaii Developmental Disabilities Division; Mary Brogan – Hawaii Developmental Disabilities Division