HHS · no agency ID · record R0712

Use of National Language Processing/Machine Learning to Identify Personal Identifiers in Health Center EHR Medication Data

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

The NLP/ML processes being used are attempting to identify personal identifiers within EHR data fields that capture medication information such as name of medication and dosage of medication.

Expected benefits

The expected benefits are that with the use of these techniques to remove any personal identifiers, more medication data can be made available in restricted use data files for researchers and interested persons to analyze, which would ultimately allow more robust data for studying medications administered/present during visits to health centers.

System outputs

The initial output provides lists/tables of person identifiers that were identified by this tool for review and/or removal from the medication data fields.

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