VA · VA-25-3152 · record R2223

Program Evaluation for Commission on Collegiate Nursing Education (CCNE)

Department of Veterans Affairs · VHA: Veterans Health Administration

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: Not reported
Withheld from public reporting: Not reported

Mission

Topic area: Other
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

To better understand and manage large volumes of written data, that assist in identifying student trends and curriculum gaps, and evaluating the program, utilizing CCNE standards and expected outcomes.

Expected benefits

- Improved patient safety by enhancing learner outcomes and assisting and guiding nursing resident progress from advanced beginner to competent professional. - Increased efficiency by reducing the labor hours spent evaluating large volumes of data. The saved time can be shifted to curriculum development, resident oversight, and program improvement. - Assisting in attaining Registered Nurse Transition-to-Practice (RNTTP) program CCNE accreditation.

System outputs

The reports identify positive program trends and areas of improvement. AI will also be used in evaluating resident trajectory utilizing Patricia Benner's novice to expert theory (NTE). VAGPT as tested has already demonstrated (specificity) for identifying nurse resident progress related to Benner's NTE.

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: 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: 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