HHS · no agency ID · record R0707

HIV Data Quality Score (DQS) Project

Department of Health and Human Services · HHS/CDC

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Health and Medical
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Currently, 60 health departments and 150 community-based organizations submit National HIV Monitoring and Evaluation (NHM&E) program data using a standard online form. However, this data often contains errors that require significant manual cleanup by CDC's HIV data managers. To address this issue, our project aims to create a large language model (LLM)-based data quality score capable of detecting errors in datasets and measuring dataset cleanliness levels. LLMs can also be utilized to automatically fix some detected errors.

Expected benefits

This project intends to enable HIV data managers to quickly identify errors, track trends in data quality by site, provide targeted technical assistance (TA), and automate some error corrections.

System outputs

The outputs include both a List of identified erroneous data fields in a dataset and a Dataset with some errors automatically corrected. This will be available for future evaluation efforts.

Technology

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

Sourcing

How it was built: Not reported
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: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
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