Bridging the Literacy Gap for Surgical Consents: An AI-Human Expert Collaborative Approach

Abstract

Abstract Background Despite the importance of informed consent in healthcare, the readability and specificity of consent forms often impedes patients’ comprehension. Health literacy is linked to patient outcomes, making it essential to address these issues. This study investigates the use of GPT-4 to simplify surgical consent forms and introduces an AI-human expert collaborative approach to validate content appropriateness. Methods Consent forms from multiple institutions were assessed for readability and simplified using GPT-4, with pre- and post-simplification readability metrics compared using nonparametric tests. Independent reviews by medical authors and a malpractice defense attorney were conducted. Finally, GPT-4’s potential for generating de novo procedure-specific consent forms was assessed, with forms evaluated using a validated 8-item rubric and expert subspecialty surgeon review. Results Analysis of 15 academic medical centers’ consent forms revealed significant reductions in average reading time, word rarity, and passive sentence frequency (all P <0.05) following GPT-4-faciliated simplification. Readability improved from an average college freshman to an 8th-grade level ( P =0.004), matching the average American’s reading level. Medical and legal sufficiency consistency was confirmed. GPT-4 generated procedure-specific consent forms for five varied surgical procedures at an average 6th-grade reading level. These forms received perfect scores on a standardized consent form rubric and withstood scrutiny upon expert subspeciality surgeon review. Conclusions This study demonstrates the first AI-human expert collaboration to enhance surgical consent forms, significantly improving readability without sacrificing clinical detail. Our framework could be extended to other patient communication materials, emphasizing clear communication and mitigating disparities related to health literacy barriers. Ensuring AI technologies are safely incorporated into clinical practice is crucial to reach a wide range of patients, including the most vulnerable. Data Availability Statement The raw data was not publicly deposited due to incorporating proprietary datasets, such as the Corpus of Contemporary American English. However, data are available on request for replicability. Code Availability Statement The code used for these analyses are available from the authors on request.

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