Applying and Testing a Crowdsourcing Platform to Support Home-Based Simulation.

Area of Science:

• Medical Simulation and Training
• Educational Technology
• 3D Printing Applications

Background:

• Traditional in-person, centralized laboratory simulation delivery presents logistical and scalability challenges.
• The COVID-19 pandemic accelerated the need for decentralized, remote learning solutions in medical education.
• Existing simulation methods are often costly and inaccessible for widespread home-based adoption.

Purpose of the Study:

• To identify challenges in transitioning simulation delivery from in-person to decentralized, online formats.
• To propose a crowdsourcing framework utilizing community-based 3D printers for affordable simulator production.
• To present exploratory research and a test case evaluating the feasibility of this crowdsourcing approach.

Main Methods:

• Development of a crowdsourcing model for producing low-cost medical simulators using distributed 3D printing resources.
• Conducting a test case study to assess the practical application and scalability of the proposed framework.
• Analysis of successes and areas for improvement identified during the test case implementation.

Main Results:

• The test case demonstrated the potential of crowdsourced 3D printing to scale decentralized simulation practices.
• Identified key successes in leveraging community resources for simulator production.
• Highlighted areas requiring further theoretical and empirical research for full implementation.

Conclusions:

• A crowdsourcing model using 3D printers is a viable strategy for affordable, decentralized simulation delivery.
• This approach offers a scalable solution for medical training during and after public health crises.
• Further research and testing are necessary to optimize and fully implement this innovative training model.