Filer Evaluation prioritization using risk-based decision Machine Learning approach

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

2023-01 (month precision)Source: Jan-23

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Information Technology

Operational functions

Risk scoring and triage Derived by keyword rules; see methodology

Problem the AI is intended to solve

The FDA's Office of Information Operations (OIO) has an opportunity to enhance its evaluation capabilities across over 4,000 filers in the current FDA inventory by implementing a systematic, data-driven approach to risk assessment and prioritization. By developing standardized evaluation processes and integrated analytical tools, OIO can optimize resource allocation, improve consistency in risk identification, and strengthen the FDA's capacity to effectively protect public health through targeted regulatory oversight.

Expected benefits

OIO is responsible for filer evaluations. There are over 4,000 filers in the current FDA inventory and this ML-based risk scoring approach to identify high-risk filers reduces the burden of sorting through the information manually and provides a standard process for conducting evaluations for the staff. An interactive dashboard has been developed that displays model outputs in various forms for staff use.

System outputs

The ML-based model provides a complete list of filers with all the relevant information along with their relative risk scores for FDA staff to conduct evaluation of the filers.

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)

evaluation prediction ml

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Contract and in-houseSource: c) Developed with both contracting and in-house resources

Vendor (as reported)

Precise Software Solutions, Inc.

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

Yes

Privacy Impact Assessment

Not reported

Authorization to Operate

Yes

Demographic features

k) None of the above

Training and evaluation data

Import operations data including but not limited to filer evaluation history, corrections to transmitted data, database lookup failures, filer table record creation dates, PREDICT scores

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.

Potential impacts and how they were identified

Not reported