IVC filter – assessing the readability and quality of patient information on the Internet

Ko, Tsz Ki; Yun Tan, Denise Jia; Hadeed, Sebastian

doi:10.1016/j.jvsv.2023.101695

Journal of Vascular Surgery: Venous and Lymphatic Disorders

2024

DOI: 10.1016/j.jvsv.2023.101695

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IVC filter – assessing the readability and quality of patient information on the Internet

Tsz Ki Ko,

Denise Jia Yun Tan,

Sebastian Hadeed

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2024

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Leveraging Generative Artificial Intelligence Models in Patient Education on Inferior Vena Cava Filters

Singh,

Jamal,

Qureshi

et al. 2024

Clinics and Practice

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Background: Inferior Vena Cava (IVC) filters have become an advantageous treatment modality for patients with venous thromboembolism. As the use of these filters continues to grow, it is imperative for providers to appropriately educate patients in a comprehensive yet understandable manner. Likewise, generative artificial intelligence models are a growing tool in patient education, but there is little understanding of the readability of these tools on IVC filters. Methods: This study aimed to determine the Flesch Reading Ease (FRE), Flesch–Kincaid, and Gunning Fog readability of IVC Filter patient educational materials generated by these artificial intelligence models. Results: The ChatGPT cohort had the highest mean Gunning Fog score at 17.76 ± 1.62 and the lowest at 11.58 ± 1.55 among the Copilot cohort. The difference between groups for Flesch Reading Ease scores (p = 8.70408 × 10−8) was found to be statistically significant albeit with priori power found to be low at 0.392. Conclusions: The results of this study indicate that the answers generated by the Microsoft Copilot cohort offers a greater degree of readability compared to ChatGPT cohort regarding IVC filters. Nevertheless, the mean Flesch–Kincaid readability for both cohorts does not meet the recommended U.S. grade reading levels.

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Leveraging Generative Artificial Intelligence Models in Patient Education on Inferior Vena Cava Filters

Singh,

Jamal,

Qureshi

et al. 2024

Clinics and Practice

View full text Add to dashboard Cite

show abstract

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IVC filter – assessing the readability and quality of patient information on the Internet

Cited by 1 publication

References 49 publications

Leveraging Generative Artificial Intelligence Models in Patient Education on Inferior Vena Cava Filters

Leveraging Generative Artificial Intelligence Models in Patient Education on Inferior Vena Cava Filters

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