HHS · no agency ID · record R0662

Autocoding to Support Adverse Drug Event Surveillance

Department of Health and Human Services · HHS/CDC

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: 2024-05 (month precision)Source: May-24
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Administrative Functions
Operational functions: Clinical and medical Derived by keyword rules; see methodology

Problem the AI is intended to solve

Manual coding of adverse drug event reports is time-consuming and slows down the production of prevalence estimates. The AI model will automate and speed up the coding process for surveillance epidemiologists.

Expected benefits

The AI model will help epidemiologists quickly determine whether reported adverse drug events meet surveillance case definitions, speeding up the coding process and enabling faster, more accurate prevalence estimates for the surveillance system.

System outputs

The model takes a de-identified free-text description of a patient's emergency department visit, along with other pre-coded variables, and outputs the probability that the encounter meets the surveillance case definition for an adverse drug event.

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): Not reported
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: No
Demographic features: k) None of the above
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