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Improving data capture in clinical research using a chatbot

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R41LM013419-01
Agency Tracking Number: R41LM013419
Amount: $252,130.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: NLM
Solicitation Number: PA19-270
Timeline
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-09-08
Award End Date (Contract End Date): 2021-09-07
Small Business Information
135 CANNON ST STE 101
Charleston, SC 29425-8909
United States
DUNS: 117009052
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 BRANDON WELCH
 (843) 792-5452
 welchbm@musc.edu
Business Contact
 BRANDON WELCH
Phone: (843) 518-3769
Email: brandon@dokbot.io
Research Institution
 MEDICAL UNIVERSITY OF SOUTH CAROLINA
 
1 SOUTH PARK CIRCLE - BUILDING 1SUITE 506
CHARLESTON, SC 29407
United States

 Nonprofit college or university
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 participantsPROJECT NARRATIVE .
Data capture tools for clinical research that provide high-quality information at a low cost are needed. Dokbot
is a simple, scalable tool that uses intuitive text-based conversations to collect data from patients. The goal of
this project is to develop a functional prototype of Dokbot and demonstrate the feasibility as an approach for
data collection in clinical research settings.

* Information listed above is at the time of submission. *

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