Improving data capture in clinical research using a chatbot

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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