Improving data capture in clinical research using a chatbot

使用聊天机器人改进临床研究中的数据捕获

基本信息

批准号：
10016887
负责人：
Brandon M Welch
金额：
$ 25.21万
依托单位：
DOKBOT, LLC
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-08 至 2022-09-07
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10016887
关键词：
Administrator Award Clinical Clinical Data Clinical Research Consumption Data Data Collection Data Reporting Development Disadvantaged Evaluation Research Funding Goals Healthcare Human Interview Interviewer Intuition Mediating Medical Methods Outcome Paper Participant Patient Outcomes Assessments Patients Performance Persons Phase Principal Investigator Questionnaires Research Research Personnel Research Priority Research Support Self Administration Self Assessment Small Business Innovation Research Grant Small Business Technology Transfer Research South Carolina Surveys Technology Testing Text Time Translational Research United States National Institutes of Health Universities base chatbot comparative effectiveness cost data quality design effectiveness clinical trial experience handheld mobile device improved innovation iterative design new technology novel prototype response satisfaction therapy development tool usability

项目摘要

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

项目摘要。直接从研究参与者收集完整而准确的结果数据正在变得越来越多重要的。临床研究人员需要一种成本效率的方法来捕获高质量的患者报告结果。通常，直接从参与者捕获的数据是通过自我管理的问卷或通过人类面试官，每个面试官都有自己的优势和缺点。有效的新数据捕获可以通过在人类访谈的参与下收集患者报告结果的技术自我管理调查的成本将增强临床研究的巨大能力。 Dokobot，LLC和南卡罗来纳州医科大学（MUSC）已合作开发Dokobot，这是一个简单，可扩展的聊天机器人使用基于文本的对话来收集临床研究参与者的数据在其移动设备上浏览器。聊天机器人是一种捕获临床数据的创新和有效的方法研究。不幸的是，当前的聊天机器人技术不能充分支持临床中的数据捕获研究。可以对Dokobot进行调整以增强临床研究中的数据捕获。但是，重要的是需要适应，改进和修复来扩展和优化Dokobot，以理想地支持它临床研究。为了实现这一目标，我们首先需要了解临床的机会和障碍研究利益相关者使用Dokobot（AIM 1），然后适应并迭代地重新定义临床研究的Dokobot（AIM 2）。通过证明Dokobot作为简单，低成本的可行性在临床研究环境中收集数据的方法，我们将有一条清晰的途径来开发技术，专业知识和证据对改善研究临床数据收集产生重大影响。和通过STTR奖的支持，Dokobot可以成为帮助临床研究人员改善的有效工具研究参与者的数据质量和效率