HHS · no agency ID · record R0818

Analytics-Driven Supplement Evaluation (ASE)

Department of Health and Human Services · HHS/FDA/CDER

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2024-03 (month precision)Source: Mar-24
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Health and Medical
Operational functions: Risk scoring and triage Derived by keyword rules; see methodology

Problem the AI is intended to solve

Exponential increase in post-approval chemistry, manufacturing, and controls (CMC) change submissions, with 80% being Changes Being Effected (CBE-30/0) notifications that may be suitable for systematic analytics-driven evaluation.

Expected benefits

This AI use case supports the triage and staff assignment process for the review of post-market Change Being Effected (CBE) supplement submissions, improving review efficiency and consistency while ensuring appropriate regulatory oversight.

System outputs

Using a Convolutional Neural Network (NN) model, in combination with a rules-based approach, produces an output that helps staff triage CBE submission review

Technology

AI classification: Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.
System name(s): Other/CDEROne
Custom-developed code: Yes
Public source code: Not reported

Sourcing

How it was built: Contract and in-houseSource: c) Developed with both contracting and in-house resources
Vendor (as reported): In-house (CDER staff)
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: Yes
Demographic features: k) None of the above
Training and evaluation data: Data submitted in applicants' supplemental submissions
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported