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Medical Students' Perception of Automated Note Feedback After Simulated Encounters.

Saurabh K Bansal¹, Manajyoti Yadav¹, Jianing Zhou²

¹Department of Internal Medicine, University of Illinois College of Medicine at Peoria, Peoria, Illinois, USA.

The Clinical Teacher

|November 18, 2025

Summary

This summary is machine-generated.

Automated grading systems using natural language processing (NLP) offer a potential tool for formative feedback on medical student patient notes (PNs). While visually appealing, this NLP feedback did not surpass traditional methods in quality.

Keywords:

automated feedback medical student model notes natural language processing (NLP)

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Area of Science:

Medical Education Technology
Natural Language Processing in Healthcare
Clinical Documentation Improvement

Background:

Grading medical student patient notes (PNs) is time-consuming.
Natural language processing (NLP) presents a potential solution for automated grading.
This study explored the value of automated NLP-based feedback on PNs.

Purpose of the Study:

To deploy and evaluate an automated NLP system for grading medical student PNs.
To assess the perceived value of automated feedback compared to traditional methods.
To explore student perceptions of feedback quality and utility.

Main Methods:

An NLP system graded PNs from third-year medical students after standardized patient encounters.
Individualized feedback reports on 'items found' and 'items not found' were generated.
Students received either automated feedback or faculty-written model note feedback first.

Main Results:

Qualitative analysis indicated automated feedback was visually appealing and aided comparison of documentation.
Students found the automated feedback helpful for improving documentation skills.
Model notes were perceived as trustworthy, and automated feedback quality did not surpass it.

Conclusions:

Automated NLP systems show potential for formative feedback on note writing.
Current automated systems may not surpass traditional feedback methods in quality.
Order effects and small sample size limit generalizability; software had occasional errors.