Maddipatla, VignithaNeal, TiffanyGottipati, MounikaSwiezy, Naomi2025-07-072025-07-072025-05-09Maddipatla, V., Neal, T., Gottipati. M., & Swiezy, N. (2025). Analyzing Participant Feedback on various training components to enhance future HANDS Intensive trainings (2006-2025). Presented at the Spring 2025 Poster Session, Luddy School of Informatics, Computing, and Engineering, Department of Biomedical Engineering and Informatics, Indiana University Indianapolis.https://hdl.handle.net/1805/49253This project analyzed nearly two decades of participant feedback from HANDS in Autism® Intensive Trainings conducted between 2006 and 2025. The goal was to identify satisfaction trends and improvement opportunities in training logistics, content, communication, and participant engagement. Using REDCap datasets, the data was cleaned, standardized, and analyzed using Python, Power BI, and Excel. Results revealed consistently high satisfaction scores (averaging 4.8/5), with increased engagement over the course of each training week. Top-rated components included speaker knowledge and small group activities, while lecture engagement showed room for improvement. The project demonstrated the value of health informatics in translating large-scale feedback into actionable insights and highlighted the importance of data-driven strategies to enhance the delivery of autism-focused professional training programs.en-USAutismAutism Spectrum Disorder (ASD)Evidence-based practices (EBP)PsychiatryIndividuals with ASDBehavioral Outcome AnalysisFeedback AnalysisTraining EvaluationData VisualizationLongitudinal Data AnalysisSurvey Trend AnalysisPoster