Analyzing client denial trends in the NDI dataset: Patterns and predictive insights

dc.contributor.authorSamala, Vishwasree
dc.contributor.authorNeal, Tiffany
dc.contributor.authorDeodhar, A
dc.contributor.authorDevarapalli, Baby Amulya
dc.contributor.authorSwiezy, Naomi
dc.date.accessioned2025-04-22T18:37:14Z
dc.date.available2025-04-22T18:37:14Z
dc.date.issued2024-05
dc.description.abstractThis project analyzed denial patterns among clients in the HANDS in Autism® NDI Exploratory dataset. Using REDCap and Cerner data, a structured coding scheme was implemented for consistent data entry and scoring. Python was used to quantitatively analyze denial reasons across 2021–2023. The most frequent denial factors included unmet family/parent criteria and issues unrelated to autism. Statistical testing, including Chi-Square and Fisher’s exact tests, revealed no significant relationship between gender and denial reasons. The project also produced a user guide for REDCap data entry and proposed future directions, including expanding the dataset and improving data completeness through enhanced data collection practices.
dc.identifier.citationSamala, V., Neal, T., Deodhar, A., Devarapalli, B. A., & Swiezy, N. (2024). Analyzing client denial trends in the NDI dataset: Patterns and predictive insights. Presented at the Spring 2024 Poster Session, HANDS in Autism® Interdisciplinary Training and Resource Center at Indiana University School of Medicine, Indiana University Indianapolis. †
dc.identifier.urihttps://hdl.handle.net/1805/47338
dc.language.isoen_US
dc.subjectAutism
dc.subjectAutism Spectrum Disorder (ASD)
dc.subjectClient Denial Patterns
dc.subjectDevelopmental Disabilities
dc.subjectData-Driven Insights in Autism
dc.subjectEvidence-Based Decision Making
dc.titleAnalyzing client denial trends in the NDI dataset: Patterns and predictive insights
dc.typePresentation
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