Program Evaluation for Commission on Collegiate Nursing Education (CCNE)
Description
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.
Detailed example
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.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
- 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.
Controls / human review
ATO: Not reported; PIA: Not published