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A Patient-Focused Comparative Analysis of Large Language Models for Epidural Analgesia Education
Akshar P Patel1, Stanley Joseph1, Ashna Bhardwaj1
1From the Medical College of Georgia, Augusta University, Augusta, Georgia.
A&A Practice
|August 5, 2025
Summary
This study compared two AI chatbots for patient education on epidural analgesia. MediSearch offered more reliable information, while ChatGPT provided easier-to-understand explanations, revealing a trade-off between accuracy and readability.
Area of Science:
- Medical Education
- Artificial Intelligence in Healthcare
- Obstetrics
Background:
- Patient education on epidural analgesia is crucial for informed consent and managing expectations.
- Current educational methods may not adequately address patient concerns regarding risks and benefits.
- Large language models (LLMs) present a potential avenue for improving patient information delivery.
Purpose of the Study:
- To evaluate the effectiveness of two LLMs, ChatGPT and MediSearch, in educating patients about epidural analgesia.
- To compare the reliability and readability of responses generated by these AI tools.
Main Methods:
- 100 patient-focused questions regarding epidural analgesia were formulated using the Rothwell System.
- Responses from ChatGPT and MediSearch were assessed using DISCERN reliability scores.
- Readability was measured using Flesch-Kincaid Grade Level and Coleman-Liau Index.
Main Results:
- MediSearch demonstrated significantly higher reliability scores compared to ChatGPT (P < .0001).
- ChatGPT achieved superior readability, indicated by lower Flesch-Kincaid Grade Level and Coleman-Liau Index scores (P =.0013).
- An imbalance was observed between the reliability and readability of the two LLMs.
Conclusions:
- While both LLMs show potential for patient education, neither optimally balances reliability and readability for epidural analgesia information.
- Healthcare providers should be aware of these differing strengths when considering AI tools for patient education.
- Further development is needed to enhance AI-generated medical information for both accuracy and accessibility.
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