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Updated: May 22, 2026

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
An analysis of artificial intelligence heart failure discharge instructions compared to routinely referenced
Evan Derector1, Daniel Ricketti2, Anupam A Kumar3
1Cooper Medical School of Rowan University, Camden, NJ, USA.
Background:
Heart failure (HF) affects 1-3% of adults globally with high hospital re-admission rates and healthcare costs. Research aims to improve the discharge process to reduce post-discharge complications.
Objectives:
This study compares the quality, readability, and health literacy requirements of artificial intelligence (AI) generated discharge instructions with commonly accessed patient education materials.
Methods:
A blinded survey of 15 cardiovascular disease fellows evaluated HF discharge instructions generated from WebMD, Lexicomp®, and ChatGPT-4o. Quality was assessed using a modified DISCERN criteria and the Global Quality Scale (GQS) while readability was analyzed using the Flesch Reading Ease Score (FRES), Flesch-Kincaid Reading Grade Level (FKGL), and the Gunning-Fog Index (GFI). Statistical analysis identified the highest quality patient-centered discharge materials and reading level requirements.
Results:
Quality metrics showed no statistical differences. However, ChatGPT-4o was rated as "good" while WebMD and Lexicomp® were rated as "excellent." Importantly, expert reviewers identified no clinical inaccuracies in the AI-generated text. Readability analysis found ChatGPT-4o-generated instructions required college-level literacy, significantly higher than WebMD and Lexicomp® (8th-9th grade). Qualitative comparison revealed that WebMD and Lexicomp® utilized direct, behavioral commands, whereas AI-generated text relied on abstract cognitive tasks, contributing to the higher literacy burden.
Conclusion:
While all sources provided high-quality information, AI-generated materials required significantly higher literacy levels. This study highlights the need for improved readability in patient education materials. Future study should analyze prompt engineering strategies that prioritize concrete examples over abstract concepts, ensuring that high-quality AI tools are accessible to patients with lower health literacy.
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