Machine Learning for Proactive IPE Compliance: Predictive Analytics That Reduce Service Gaps by 30 Days

Prompt implementation of Individualized Plans for Employment (IPEs) is crucial for guaranteeing that individuals with disabilities have continuous and efficient vocational rehabilitation assistance. Prolonged IPE development and approval may result in service interruptions, diminished customer involvement, and failure to adhere to federally specified deadlines. This study examines the use of machine learning (ML) models to proactively detect and address potential service deficiencies in Individualized Plan for Employment (IPE) timelines inside state rehabilitation agencies. Employing an extensive dataset of historical case management records, we constructed and tested classification and time-series forecasting models designed to identify early risk indicators for IPE delays. The classification model had superior predictive accuracy, with an F1-score of 0.82, whilst the forecasting model offered 30-day advance alerts regarding prospective non-compliance incidents. These predicted insights allowed counselors and administrative teams to intervene earlier, therefore decreasing the average duration of service gaps by 30 days. The paper delineates the structure of an AI-driven compliance monitoring framework that may be assimilated with current state workforce and case management systems, such as AWARE and other Vocational Rehabilitation (VR) platforms. This scalable system utilizes predictive analytics to automate risk identification, prioritize caseloads, and facilitate data-driven decision-making for enhancements in service delivery. This research enhances the existing knowledge on AI applications in public sector human services and illustrates the potential of machine learning-driven early warning systems to improve operational compliance, client outcomes, and timely access to vocational rehabilitation programs for individuals with disabilities.