HHS · no agency ID · record R0695

Machine Learning with Premier Healthcare Data to inform predictive modeling of antibiotic use

Department of Health and Human Services · HHS/CDC

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: 2025-04 (month precision)Source: Apr-25
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Health and Medical
Operational functions: Forecasting and prediction, Clinical and medical Derived by keyword rules; see methodology

Problem the AI is intended to solve

Surveillance data, including the Emerging Infections Program (EIP) and the National Healthcare Safety Network (NHSN) can be linked to and other inpatient data to better characterize risk factors and outcomes of important healthcare associated infections (HAI) and antimicrobial resistance (AR). However, discharge data often lacks detailed information about inpatient antibiotic use.

Expected benefits

This project uses the Premier Healthcare Database (PHD), an electronic health database, to predict inpatient antibiotic use and length of therapy using data readily available in claims and other electronic health record databases. This adds additional potential sources of information to support insights.

System outputs

These models will allow us to fill in gaps in antibiotic use information in Medicare claims and discharge datasets to better leverage EIP and NHSN data to better understand how cumulative antibiotic use may impact patients risk for HAIs and AR infections.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): EDAV (Enterprise Data Analytics and Visualization Platform)
Custom-developed code: No
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: a) Race/Ethnicity; b) Sex; c) Age
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