Dataset Information

Using a chat-based informed consent tool in large-scale genomic research.

ABSTRACT:

Objective

We implemented a chatbot consent tool to shift the time burden from study staff in support of a national genomics research study.

Materials and methods

We created an Institutional Review Board-approved script for automated chat-based consent. We compared data from prospective participants who used the tool or had traditional consent conversations with study staff.

Results

Chat-based consent, completed on a user's schedule, was shorter than the traditional conversation. This did not lead to a significant change in affirmative consents. Within affirmative consents and declines, more prospective participants completed the chat-based process. A quiz to assess chat-based consent user understanding had a high pass rate with no reported negative experiences.

Conclusion

Our report shows that a structured script can convey important information while realizing the benefits of automation and burden shifting. Analysis suggests that it may be advantageous to use chatbots to scale this rate-limiting step in large research projects.

SUBMITTER: Savage SK

PROVIDER: S-EPMC10797258 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Using a chat-based informed consent tool in large-scale genomic research.

Savage Sarah K SK LoTempio Jonathan J Smith Erica D ED Andrew E Hallie EH Mas Gloria G Kahn-Kirby Amanda H AH Délot Emmanuèle E Cohen Andrea J AJ Pitsava Georgia G Nussbaum Robert R Fusaro Vincent A VA Berger Seth S Vilain Eric E

Journal of the American Medical Informatics Association : JAMIA 20240101 2

<h4>Objective</h4>We implemented a chatbot consent tool to shift the time burden from study staff in support of a national genomics research study.<h4>Materials and methods</h4>We created an Institutional Review Board-approved script for automated chat-based consent. We compared data from prospective participants who used the tool or had traditional consent conversations with study staff.<h4>Results</h4>Chat-based consent, completed on a user's schedule, was shorter than the traditional conversa ...[more]

PMID: 37665746

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Project description:BackgroundInformed consent forms (ICFs) for clinical trials have become increasingly complex, often hindering participant comprehension and engagement due to legal jargon and lengthy content. The recent advances in large language models (LLMs) present an opportunity to streamline the ICF creation process while improving readability, understandability, and actionability.objectivesThis study aims to evaluate the performance of the Mistral 8x22B LLM in generating ICFs with improved readability, understandability, and actionability. Specifically, we evaluate the model's effectiveness in generating ICFs that are readable, understandable, and actionable while maintaining the accuracy and completeness.MethodsWe processed 4 clinical trial protocols from the institutional review board of UMass Chan Medical School using the Mistral 8x22B model to generate key information sections of ICFs. A multidisciplinary team of 8 evaluators, including clinical researchers and health informaticians, assessed the generated ICFs against human-generated counterparts for completeness, accuracy, readability, understandability, and actionability. Readability, Understandability, and Actionability of Key Information indicators, which include 18 binary-scored items, were used to evaluate these aspects, with higher scores indicating greater accessibility, comprehensibility, and actionability of the information. Statistical analysis, including Wilcoxon rank sum tests and intraclass correlation coefficient calculations, was used to compare outputs.ResultsLLM-generated ICFs demonstrated comparable performance to human-generated versions across key sections, with no significant differences in accuracy and completeness (P>.10). The LLM outperformed human-generated ICFs in readability (Readability, Understandability, and Actionability of Key Information score of 76.39% vs 66.67%; Flesch-Kincaid grade level of 7.95 vs 8.38) and understandability (90.63% vs 67.19%; P=.02). The LLM-generated content achieved a perfect score in actionability compared with the human-generated version (100% vs 0%; P<.001). Intraclass correlation coefficient for evaluator consistency was high at 0.83 (95% CI 0.64-1.03), indicating good reliability across assessments.ConclusionsThe Mistral 8x22B LLM showed promising capabilities in enhancing the readability, understandability, and actionability of ICFs without sacrificing accuracy or completeness. LLMs present a scalable, efficient solution for ICF generation, potentially enhancing participant comprehension and consent in clinical trials.

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Dataset Information

Using a chat-based informed consent tool in large-scale genomic research.

Objective

Materials and methods

Results

Conclusion

Publications

Using a chat-based informed consent tool in large-scale genomic research.

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