Improving data capture in clinical research using a chatbot

项目总结。 直接从研究参与者那里收集完整和准确的结果数据变得越来越多 很重要。临床研究人员需要一种成本低廉的ff方法来捕获高质量的患者-报道 结果。通常，直接从参与者那里获取的数据是通过自我管理的问卷或 通过人类面试，每个人都有自己的优势和劣势。一种有效的新数据ff 获取可通过参与人类访谈收集患者报告结果的技术 自我管理调查的成本将建立巨大的临床研究能力。Dokbot，LLC和 南卡罗来纳医科大学(MUSC)合作开发了一款简单、可扩展的Dokbot Chatbot使用基于文本的对话从临床研究参与者那里收集数据 在他们的移动设备上使用浏览器。聊天机器人是一种创新和有效的方式来捕获临床数据 研究。遗憾的是，目前的聊天机器人技术不能充分支持临床数据捕获 研究。Dokbot可以被改装成增强临床研究中的数据捕获。然而，Signifi不能 需要适应、改进和重新部署以扩展和优化Dokbot，使其能够理想地支持 临床研究。要做到这一点，我们fi首先需要了解临床界的机遇和障碍。 使用Dokbot(目标1)研究利益相关者，然后适应并迭代地重新生成fiNe的功能原型 用于临床研究的Dokbot(目标2)。通过论证Dokbot作为一种简单、低成本的可行性 在临床研究环境中收集数据的方法，我们将有一条明确的发展技术的路径， 专业知识和证据对改善用于研究的临床数据收集没有显著影响。fi。使用 通过STTR奖的支持，Dokbot可以成为帮助临床研究人员改进的ff有效工具 来自研究参与者的数据的质量和有效性ffi

PROJECT SUMMARY . Collecting complete and accurate outcome data directly from research participants is becoming increasingly important. Clinical researchers needs a cost-effective approach to capture high-quality patient-reported outcomes. Typically, data captured directly from participants is through self-administered questionnaires or through a human interviewer, each with their own advantages and disadvantages. An effective new data capture technology that can collect patient-reported outcomes with the engagement of human interviews at the cost of self-administered surveys would build tremendous capacity for clinical research. Dokbot, LLC and the Medical University of South Carolina (MUSC) have partnered to develop Dokbot, a simple, scalable chatbot that uses text-based conversations to collect data from clinical research participants using the browser on their mobile devices. Chatbots are an innovative and effective way to capture data for clinical research. Unfortunately, current chatbot technologies do not adequately support data capture in clinical research. Dokbot can be adapted to enhance data capture in clinical research. However, significant adaptation, improvement, and refinement is needed to extend and optimize Dokbot for it to ideally support clinical research. To achieve this, we first need to understand opportunities and barriers among clinical research stakeholders using Dokbot (Aim 1) and then adapt and iteratively refine a functional prototype of Dokbot for clinical research (Aim 2). By demonstrating the feasibility of Dokbot as a simple, low-cost approach for collecting data in clinical research settings, we will have a clear path to develop the technology, expertise, and evidence to make a significant impact on improving clinical data collection for research. With support through the STTR award, Dokbot could become an effective tool to help clinical researchers improve the quality and efficiency of data from research participants