J. Beattie1, S. Neufeld2, D. X. Yang2, C. Chukwuma3, N. B. Desai2, M. Dohopolski2, and S. B. Jiang2; 1Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 2Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 3Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX
Purpose/Objective(s):Understanding informed consent forms, which outline the risks, costs, and procedures of clinical trials, presents a significant challenge for patients due to their complex language and length. Recognizing that such complexity can impede patient comprehension and decision-making, this study proposes the use of Large Language Models (LLMs) to distill these forms into concise, easy-to-understand cover pages. We hypothesize that we can significantly improve the readability of these forms using LLMs.Materials/
Methods: Five approved institutional clinical trial consent forms were assessed for their readability using SMOG and the Flesch-Kincaid Ease of Reading score using Python libraries. The documents were segmented and catalogued in a vector database to facilitate similarity searches. OpenAI’s GPT-4 API was prompted to extract information about costs, payments, contact information, eligibility criteria, treatments, duration, and requirements based on relevant information retrieved from the vector database. Furthermore, each prompt included instructions to respond at a 7th to 8th-grade reading level. The answers were collected, and their readability evaluated using the aforementioned metrics. Finally, the SMOG and Flesch-Kincaid readability scores were compared using a Wilcoxon signed-rank test. Results: The original informed consent forms exhibited an average SMOG score of 16.74 (± 0.86), corresponding to a graduate reading level, and an average Flesch-Kincaid score of 12.93 (± 0.70), corresponding to an undergraduate reading level. Conversely, the LLM-generated information sheets exhibited an average SMOG score of 11.25 (± 0.50), corresponding to a high school reading level, and an average Flesch-Kincaid score of 8.15 (± 0.68), corresponding to an 8-9th grade reading level, demonstrating a significant reduction in reading level (p<0.05 for both SMOG and Flesh-Kincaid scores). Some LLM-generated information sheets were successful in adhering to a 7th to 8th-grade reading level, as denoted by a Flesch-Kincaid score < 8. Conclusion: Our approachsuccessfully simplified clinical trial consent forms using LLMs, reducing reading levels and making information more accessible. This approach could significantly enhance the transparency and comprehensibility of the clinical trial consent process, fostering a more patient-centric approach. Feedback from patients on LLM-generated information sheets could provide invaluable insights into the practicality and usefulness of this approach.
