Quick Disability Determinations Model
Description
Used to screen initial applications to identify cases where a favorable disability determination is highly likely and medical evidence is readily available to prioritize this workload and expedite case processing.
Detailed example
System output consists of probability model scores for each separate scoring service, Scoring Service for Allowance and Scoring Service for Processing Time, and the overall QDD Score.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
QDD predictive model quickly identifies and prioritizes disability claims processing for individuals with severe, life-threatening, or life-altering conditions. This approach reduces processing times, improves access to benefits for those in urgent need, and allows the agency to allocate resources more efficiently, ultimately enhancing service delivery for the public.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Model used data from disability program data sets