# Improving data capture in clinical research using a chatbot

> **NIH NIH R41** · DOKBOT, LLC · 2020 · $252,130

## Abstract

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

## Key facts

- **NIH application ID:** 10016887
- **Project number:** 1R41LM013419-01
- **Recipient organization:** DOKBOT, LLC
- **Principal Investigator:** Brandon M Welch
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $252,130
- **Award type:** 1
- **Project period:** 2020-09-08 → 2022-09-07

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10016887

## Citation

> US National Institutes of Health, RePORTER application 10016887, Improving data capture in clinical research using a chatbot (1R41LM013419-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10016887. Licensed CC0.

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