Harrison Rachel M, Lapteva Ekaterina, Bibin Anton
GenAI Lab, Ophiuchus LLC, Dover, DE, United States.
Institute of Psychology, Russian Academy of Sciences, Moscow, Russian Federation.
JMIR AI. 2024 Oct 15;3:e52974. doi: 10.2196/52974.
Brief message interventions have demonstrated immense promise in health care, yet the development of these messages has suffered from a dearth of transparency and a scarcity of publicly accessible data sets. Moreover, the researcher-driven content creation process has raised resource allocation issues, necessitating a more efficient and transparent approach to content development.
This research sets out to address the challenges of content development for SMS interventions by showcasing the use of generative artificial intelligence (AI) as a tool for content creation, transparently explaining the prompt design and content generation process, and providing the largest publicly available data set of brief messages and source code for future replication of our process.
Leveraging the pretrained large language model GPT-3.5 (OpenAI), we generate a collection of messages in the context of medication adherence for individuals with type 2 diabetes using evidence-derived behavior change techniques identified in a prior systematic review. We create an attributed prompt designed to adhere to content (readability and tone) and SMS (character count and encoder type) standards while encouraging message variability to reflect differences in behavior change techniques.
We deliver the most extensive repository of brief messages for a singular health care intervention and the first library of messages crafted with generative AI. In total, our method yields a data set comprising 1150 messages, with 89.91% (n=1034) meeting character length requirements and 80.7% (n=928) meeting readability requirements. Furthermore, our analysis reveals that all messages exhibit diversity comparable to an existing publicly available data set created under the same theoretical framework for a similar setting.
This research provides a novel approach to content creation for health care interventions using state-of-the-art generative AI tools. Future research is needed to assess the generated content for ethical, safety, and research standards, as well as to determine whether the intervention is successful in improving the target behaviors.
简短信息干预在医疗保健领域已展现出巨大潜力，但这些信息的开发缺乏透明度，且公开可用的数据集稀缺。此外，由研究人员驱动的内容创建过程引发了资源分配问题，因此需要一种更高效、透明的内容开发方法。
本研究旨在通过展示生成式人工智能（AI）作为内容创建工具的应用，透明地解释提示设计和内容生成过程，并提供最大的公开可用简短信息数据集和源代码以供未来复制我们的过程，来解决短信干预内容开发的挑战。
利用预训练的大型语言模型GPT-3.5（OpenAI），我们使用先前系统评价中确定的基于证据的行为改变技术，针对2型糖尿病患者生成了一系列关于药物依从性的信息。我们创建了一个有属性的提示，旨在符合内容（可读性和语气）和短信（字符数和编码器类型）标准，同时鼓励信息的多样性以反映行为改变技术上的差异。
我们为单一医疗保健干预提供了最广泛的简短信息库，以及第一个用生成式AI精心制作的信息库。总体而言，我们的方法产生了一个包含1150条信息的数据集，其中89.91%（n = 1034）符合字符长度要求，80.7%（n = 928）符合可读性要求。此外，我们的分析表明，所有信息都表现出与在相同理论框架下为类似场景创建的现有公开可用数据集相当的多样性。
本研究提供了一种使用最先进的生成式AI工具进行医疗保健干预内容创建的新方法。未来需要进行研究，以评估生成的内容是否符合伦理、安全和研究标准，以及确定该干预是否能成功改善目标行为。
