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Charting by Exception, or CBE, is a method of documentation used in healthcare, particularly in nursing, that focuses on documenting only significant or abnormal findings rather than recording every detail. This approach aims to streamline the documentation process, improve efficiency, and ensure that healthcare providers can quickly identify deviations from normalcy in patient assessments.
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Related Experiment Video

Updated: Jan 14, 2026

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Automated Evaluation of Reflection and Feedback Quality in Workplace-Based Assessments by Using Natural Language

Jeng-Wen Chen^1,2,3,4, Hai-Lun Tu⁵, Chun-Hsiang Chang^1,2

¹Department of Otolaryngology-Head and Neck Surgery, Cardinal Tien Hospital, Fu Jen Catholic University, New Taipei City, Taiwan.

JMIR Medical Education

|October 22, 2025

Summary

Natural language processing (NLP) models, particularly BERT, effectively evaluated narrative quality in medical education assessments. This technology aids in monitoring trends and improving feedback for competency-based training.

Keywords:

Emyway platform competency-based medical education entrustable professional activities feedback otolaryngology reflection residency workplace-based assessment

Related Experiment Videos

Last Updated: Jan 14, 2026

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Area of Science:

Medical Education
Natural Language Processing
Artificial Intelligence

Background:

Competency-based medical education (CBME) requires high-quality narrative reflections and feedback for workplace-based assessments.
Evaluating these narratives at scale presents a significant challenge for educators.

Purpose of the Study:

To develop and apply natural language processing (NLP) models for assessing the quality of resident reflections and faculty feedback.
To evaluate these models within Taiwan's nationwide Entrustable Professional Activities (EPAs) platform for otolaryngology residency training.

Main Methods:

A 4-year cross-sectional study analyzed 300 randomly sampled EPA assessments.
Two medical education experts rated narratives for relevance, specificity, and reflective language, categorizing them into four quality levels.
Logistic regression, support vector machine, and BERT models were compared for narrative quality classification.

Main Results:

The BERT model achieved 85% accuracy for binary classification of resident reflections and 92% for faculty feedback.
Longitudinal analysis showed significant increases in high-quality reflections (70.3% to 99.5%) and feedback (50.6% to 88.9%).

Conclusions:

BERT-based NLP models demonstrate moderate-to-high accuracy in evaluating narrative quality in EPA assessments.
NLP offers a valuable tool for monitoring trends and enhancing formative feedback in CBME, complementing expert review.