Related Experiment Video

Updated: May 22, 2026

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

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.

Healthcare (Amsterdam, Netherlands)
|May 20, 2026
PubMed

Insights

Artificial intelligence (AI) generated heart failure (HF) discharge instructions offer high-quality information but demand college-level literacy. Current AI tools need refinement for better patient understanding and accessibility.

Area of Science:

  • Medical Informatics
  • Patient Education
  • Health Literacy

Background:

  • Heart failure (HF) is a prevalent global condition with significant healthcare costs and high hospital readmission rates.
  • Improving the post-discharge process is crucial for reducing complications and enhancing patient outcomes.

Purpose of the Study:

  • To compare the quality, readability, and health literacy demands of AI-generated discharge instructions against established patient education materials.
  • To identify patient-centered discharge materials with optimal readability and literacy requirements.

Main Methods:

  • A blinded survey assessed discharge instructions from WebMD, Lexicomp®, and ChatGPT-4o, evaluated by 15 cardiovascular disease fellows.
  • Quality was measured using modified DISCERN criteria and Global Quality Scale (GQS).
  • Readability was analyzed using Flesch Reading Ease Score (FRES), Flesch-Kincaid Reading Grade Level (FKGL), and Gunning-Fog Index (GFI).

Main Results:

  • No statistical differences in quality metrics were found between AI-generated and traditional materials.
  • ChatGPT-4o instructions required college-level literacy, significantly higher than WebMD and Lexicomp® (8th-9th grade).
  • AI-generated text relied on abstract cognitive tasks, unlike the direct commands in WebMD and Lexicomp®, increasing the literacy burden.

Conclusions:

  • All evaluated sources provided high-quality HF discharge information, but AI materials presented a higher literacy barrier.
  • There is a critical need to enhance the readability of AI-generated patient education materials.
  • Future research should focus on prompt engineering to favor concrete examples, making AI tools more accessible to patients with lower health literacy.
Abstract

Keywords:
Discharge planningHealth literacyPatient education

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