Diagnostics for All is focused on building a simple and sustainable model for improving access to diagnostic healthcare, specifically in communities where it’s limited or overlooked. I want to combine computational tools with public health information to help determine where those gaps exist and how we can begin to bridge them, even on a small scale. The idea is to use data to identify areas where diagnostic access is low and model strategies that could help combat this problem, whether that’s through local outreach or resource allocation. Alongside the computational side, I also want to explore how low-cost, physical diagnostic tools (like paper-based tests or mobile-integrated devices) could be part of the solution. The goal of this project is not to reinvent the system, but to work with the current data and structure to build something practical: a small, measurable impact in one community that shows this approach is worth continuing. The long-term goal is to develop a platform that could be adapted and scaled, while continuing to bring together technology, healthcare, and community-centered design in a meaningful way.