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医疗保健领域的首届生成式人工智能提示马拉松:一种利用ChatGPT私有实例促进员工参与的新方法。

The First Generative AI Prompt-A-Thon in Healthcare: A Novel Approach to Workforce Engagement with a Private Instance of ChatGPT.

作者信息

Small William R, Malhotra Kiran, Major Vincent J, Wiesenfeld Batia, Lewis Marisa, Grover Himanshu, Tang Huming, Banerjee Arnab, Jabbour Michael J, Aphinyanaphongs Yindalon, Testa Paul, Austrian Jonathan S

机构信息

Department of Health Informatics, NYU Langone Health, New York, New York, United States of America.

Department of Medicine, NYU Grossman School of Medicine, New York, New York, United States of America.

出版信息

PLOS Digit Health. 2024 Jul 23;3(7):e0000394. doi: 10.1371/journal.pdig.0000394. eCollection 2024 Jul.

Abstract

BACKGROUND

Healthcare crowdsourcing events (e.g. hackathons) facilitate interdisciplinary collaboration and encourage innovation. Peer-reviewed research has not yet considered a healthcare crowdsourcing event focusing on generative artificial intelligence (GenAI), which generates text in response to detailed prompts and has vast potential for improving the efficiency of healthcare organizations. Our event, the New York University Langone Health (NYULH) Prompt-a-thon, primarily sought to inspire and build AI fluency within our diverse NYULH community, and foster collaboration and innovation. Secondarily, we sought to analyze how participants' experience was influenced by their prior GenAI exposure and whether they received sample prompts during the workshop.

METHODS

Executing the event required the assembly of an expert planning committee, who recruited diverse participants, anticipated technological challenges, and prepared the event. The event was composed of didactics and workshop sessions, which educated and allowed participants to experiment with using GenAI on real healthcare data. Participants were given novel "project cards" associated with each dataset that illuminated the tasks GenAI could perform and, for a random set of teams, sample prompts to help them achieve each task (the public repository of project cards can be found at https://github.com/smallw03/NYULH-Generative-AI-Prompt-a-thon-Project-Cards). Afterwards, participants were asked to fill out a survey with 7-point Likert-style questions.

RESULTS

Our event was successful in educating and inspiring hundreds of enthusiastic in-person and virtual participants across our organization on the responsible use of GenAI in a low-cost and technologically feasible manner. All participants responded positively, on average, to each of the survey questions (e.g., confidence in their ability to use and trust GenAI). Critically, participants reported a self-perceived increase in their likelihood of using and promoting colleagues' use of GenAI for their daily work. No significant differences were seen in the surveys of those who received sample prompts with their project task descriptions.

CONCLUSION

The first healthcare Prompt-a-thon was an overwhelming success, with minimal technological failures, positive responses from diverse participants and staff, and evidence of post-event engagement. These findings will be integral to planning future events at our institution, and to others looking to engage their workforce in utilizing GenAI.

摘要

背景

医疗众包活动(如黑客马拉松)有助于跨学科合作并鼓励创新。同行评审研究尚未考虑聚焦于生成式人工智能(GenAI)的医疗众包活动,GenAI可根据详细提示生成文本,在提高医疗机构效率方面具有巨大潜力。我们的活动,即纽约大学朗格尼健康中心(NYULH)提示马拉松,主要旨在激发并提升我们多元化的NYULH社区内的人工智能应用能力,并促进合作与创新。其次,我们试图分析参与者的经验如何受到他们之前对GenAI的接触情况的影响,以及他们在研讨会上是否收到了示例提示。

方法

举办该活动需要组建一个专家规划委员会,该委员会招募了多元化的参与者,预估了技术挑战,并筹备了活动。活动由教学环节和研讨会议组成,对参与者进行教育,并让他们尝试在真实的医疗数据上使用GenAI。为参与者提供了与每个数据集相关的新颖“项目卡片”,这些卡片阐明了GenAI可以执行的任务,并且为随机抽取的一组团队提供了示例提示,以帮助他们完成每项任务(项目卡片的公共存储库可在https://github.com/smallw03/NYULH-Generative-AI-Prompt-a-thon-Project-Cards上找到)。之后,要求参与者填写一份包含7点李克特式问题的调查问卷。

结果

我们的活动成功地以低成本且技术上可行的方式,对我们机构内数百名热情的现场和虚拟参与者进行了关于GenAI负责任使用的教育和启发。所有参与者对每个调查问题的平均回答都是积极的(例如,对他们使用和信任GenAI的能力的信心)。至关重要的是,参与者报告称,他们自我感觉在日常工作中使用和推广同事使用GenAI的可能性有所增加。在收到带有项目任务描述的示例提示的人员的调查中,未发现显著差异。

结论

首届医疗提示马拉松取得了巨大成功,技术故障极少,不同参与者和工作人员反应积极,并有活动后参与度的证据。这些发现对于我们机构未来活动的规划以及其他希望让员工参与使用GenAI的机构来说至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f7a/11265701/7acbd666bf5e/pdig.0000394.g001.jpg

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