HHS · no agency ID · record R0720

Data Standardization with LLM

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: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

The purpose of this project is to enhance data standardization efforts by leveraging large language models. to improve data cleaning and standardization processes, ultimately enhancing the overall efficiency and accuracy of data

Expected benefits

Improve data cleaning and standardization processes within our data systems. ? Enhance the accuracy and reliability of data stored. Streamline workflows by automating certain tasks related to data cleaning and standardization. ? More specifically, the ultimate scope of this project includes integrating into the existing infrastructure of FluLIMS. The data as well as rules . For example, sometimes we have misspelled locations or various ways of referring to the same place (i.e., ATL vs Atlanta) and we would like to standardize that. ? By implementing this proposed project, we anticipate significant improvements in data cleaning and standardization processes within FluLIMS, leading to enhanced efficiency, accuracy, and overall effectiveness in managing flu-related information.? This will most likely lead to up to at least 50% reduction in time and effort to clean data. This can be used for other cleaning other data or other processes that LLM would be useful for.

System outputs

This would result in a higher quality dataset with increased standardization and increasing usability of the insights for staff.

Technology

AI classification: Agentic AISource: Agentic AI: AI systems that perform tasks or make decisions autonomously with minimal human intervention.
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