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Massachusetts Institute of Technology

Framework Development for Remote Clinical Trials: Assembly Process Design

Abstract

dc:description.abstract

Clinical trials are facing new difficulties after the COVID-19 outbreak, with enrollment decreasing by about half after January 2020. Tufts Clinical and Translational Research Center has partnered with the Massachusetts Institute of Technology to develop a framework for a “clinical-trial-in-a-box” for use in fully remote clinical trials, which would reduce risk of infection due to the lack of physical contact. The goal is to increase enrollment and create a safe system in which to conduct medical research while requiring as little knowledge of technology from the participant as possible, in order to include the most potential participants and eventually expand into rural regions. This thesis focuses on the assembly process of this framework, detailing the steps from receiving trial materials to shipping out the finished trial box. For more depth into the other aspects of this project, please refer to the other theses from the project group: Imane Ait Mbiriq, Carly Smith, and Joyce Noh.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2022

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Lin, Ryan
Advisor dc:contributor.advisor
  • Anthony, Brian

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright retained by author(s)

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/147318
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/147318

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Lin, Ryan. Framework Development for Remote Clinical Trials: Assembly Process Design. Massachusetts Institute of Technology, 2022. https://hdl.handle.net/1721.1/147318