Parkinson's disease (PD) is a chronic neurodegenerative disorder with progressive motor symp-
toms (tremor, rigidity, gait freezing, falls) and disabling non-motor features such as depression,
anxiety, sleep problems. Patients may suddenly deteriorate (severe OFF episodes, unexpected
falls...) and require rapid medical intervention, often outside hospital settings. This creates
a strong need for timely detection of crises and efficient dispatch of appropriate emergency
resources. Existing research on EMS in PD makes little use of longitudinal clinical information
and digital biomarkers, rely on triage decisions based on incomplete data, and do not fully
exploit modern connectivity (Internet of medical things, wearables, V2X). In this work,
we propose an Intelligent Dispatch framework that connects smartphone-based monitoring,
machine learning (ML), eXplainable AI (XAI) and optimization within the context of a crisis
related to Parkinson's disease. The framework aims to detect clinically relevant deteriora-
tion in real time, classify patients into urgency levels, provide interpretable explanations of
risk, and use these predictions to guide ambulance and hospital selection, ultimately reducing
time-to-intervention and improving coordination of emergency care for people with PD.