JMIR Publications

ABSTRACT

Background:

Heart failure management involves comprehensive lifestyle modifications such as daily weights, fluid and sodium restriction, and blood pressure monitoring placing additional responsibility on patients and caregivers with successful adherence often requiring extensive counseling and understandable patient education materials (PEMs). Prior research has shown PEMs related to cardiovascular disease often exceed the American Medical Association’s 5th-6th grade recommended reading level. The large language model (LLM) Chat Generative Pre-trained Transformer (ChatGPT) may be a useful tool for improving PEM readability.

Objective:

We aim to assess the readability of heart failure PEMs from prominent cardiology institutions and evaluate GPT-4's ability to improve these metrics while maintaining accuracy and comprehensiveness.

Methods:

A total of 143 heart failure PEMs were collected from the websites of the top 10 institutions listed on the 2022-2023 US News & World Report for “Best Hospitals for Cardiology, Heart & Vascular Surgery”. PEMs were individually entered into GPT-4 (Version updated 20 July 2023) preceded by the prompt “please explain the following in simpler terms”. The readability of the institutional PEM and ChatGPT revised PEM were both assessed using Textstat library in Python and the Textstat readability package in R software. The accuracy and comprehensiveness of revised GPT-4 PEMs were assessed by a board-certified cardiologist.

Results:

The Flesch-Kincaid grade reading level’s interquartile range spanned from 8th grade to college freshman with a median of 10th grade vs 6th to 8th grade with a median of 7th grade for institutional PEMs and GPT-4 PEMs (p< 0.001), respectively. There were 13/143 (9.1%) institutional PEMs below the 6th grade reading level which improved to 33/143 (23.1%) after revision by GPT-4 (p<0.001). No GPT-4 revised PEMs were graded as less accurate or less comprehensive compared to institutional PEMs. A total of 33/143 (23.1%) GPT-4 PEMs were graded as more comprehensive.

Conclusions:

GPT-4 significantly improved the readability of institutional heart failure PEMs. The model may be a promising adjunct resource in addition to care provided by a licensed healthcare professional for patients living with heart failure. Further rigorous testing and validation is needed to investigate its safety, efficacy and impact on patient health literacy.