HHS · no agency ID · record R0829

Process Large Amount of Submitted Docket Comments

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

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Overview

Development stage: PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2023-06 (month precision)Source: Jun-23
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Information Technology
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Deduplication of public comments, and generating draft sentiment analysis and grouping of comments

Expected benefits

To enhance the automatic process of dockets, we have created an AI/ML tool in CBER/HIVE that automatically download dockets and process them to accelerate the review of docket comments, significantly improving the efficiency and accuracy of our regulatory processes. Efficiently generate customized query outputs of VAERS queries for reviewers.

System outputs

To provide reviewers adhoc VAERS queries.

Technology

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

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): Not reported
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: Public comments on various FDA dockets
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