Enhanced Direct Enrollment Outlier Detection
Description
(Marketplace) Enhanced Direct Enrollment (EDE) allows consumers to apply for and enroll in an exchange plan directly through an approved partner's UI, without being redirected through the Healthcare.gov application. These partner systems directly interface with the APIs developed by the FFE. As EDE partners gain more control over their application process, the FFE must ensure program integrity.
Detailed example
Ensure FFE EDE program integrity
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
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Implement ML to identify anomalies/quality issues with partner-submitted person, application, and policy data?.
Audit / financial statement impact
The Enhanced Direct Enrollment Outlier Detection use-case is not considered high-impact as it does not serve as the basis for decisions or actions that affect civil rights, liberties, privacy, critical resources, safety, or strategic assets. The AI models within this use-case use machine learning techniques to detect anomalous or inconsistent patterns relative to standard partner actions and other application channels outside of the EDE pathway. The findings are shared with the agency divisions in the form of different reports and tables. The data alone is not enough to determine fraud, however, can be used by CMS in tandem with other data to determine if CMS should take any corrective actions. CMS makes all determinations on actions.
Controls / human review
ATO: Not reported; PIA: Not published