Adjsaver by Neel Kurupassery

We address a widespread lack of cost information by providing an distance-based ordered checklist for the visitation of multiple facilities, providing a tangible benefit to underserved communities.

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Inspiration

When tackling the issue of COVID-19 healthcare in underserved communities, we must first-and-foremost aim to provide tangible benefits. Quite simply, our goal is to have our target audience reach a successful conclusion--that the individuals of underserved regions reach the point of receiving testing, vaccination and/or treatment for COVID-19. To this end, the blatant hurdles are cost and distance.

If both of these are not tackled, the affected individuals will not pursue further healthcare. IQVIA Healthcare Locator SDK does provide distance metrics with 'Near Me', but how can we best utilize or expand on this? And with a lack of readily available cost information further complexified with insurance effects and lack of standardized pricing, how do we deal with the white elephant of cost?

This is where we must face the reality that most of these individuals will have to travel to not just one but multiple facilities to gain true pricing information. They may also have deal with overcrowding of a singular facility on the particular day and travel elsewhere regardless. Without a roadmap that eases and assumes the visiting of multiple facilities they will likely quit after the first failed attempt and be unwilling to try again.

Our proposed application is the use of graph theory and available tools to provide an order for names of nearest facilities based on the shortest path between them. In its ideal form, the user will end up with a recommended list of facilities to visit until they receive treatment. By having a framework that assumes the necessity of visiting multiple locations like a checklist, the user will be more motivated to follow through should the first (or first several) fail.

The simplest implementation is the use of the lat-lon coordinates of the healthcare locations to provide groupings of facilities within close proximity to each other. This mitigates the computational complexity and varying nature of road layouts in favor of a more universal solution. However tools do exist to optimize the computations with latest road layouts with Google Maps (and road conditions with Waze) and for larger numbers of groupings should the project idea prove promising and wish to be extended.

Personal Inspiration

I, as well as many others, have seen family members pass away from COVID-19 (my uncle). My grandmother who also tested positive did not hear of his death until it had already happened in the hospital and he was cremated. I have family members who've scheduled an appointment for vaccination, only to be turned away on the day of. That is why for this project I sought to create a simple app that could run on any device and utilize human nature and psychology as well as the reality of healthcare pricing to lead to better outcomes.

What it does

The application uses facility location data from the IQVIA Healthcare Locator SDK to create an adjacency matrix of distances. Subsequent calculations leaves the user with a list of facilities to visit in tandem, giving them motivation and confidence to continue their search for healthcare through potential failures due to overcrowding or overpricing at a particular facility.

How we built it

We built it using Javascript, Bootstrap, and Healthcare Locator SDK. A simple format that runs on any device.

Challenges we ran into

The biggest challenge we faced was innovating an idea that had potential. Once we had that, we had all the motivation needed to debug and complete the project.

Accomplishments that we're proud of

We're proud of creating an app very universal in nature that provides a quality-of-life improvement.

What we learned

We dived into mathematics, computer science, medicine, and global health (a concoction that could be described as nothing less than "mental soup").

What's next for Adjsaver by Neel Kurupassery

Further benefits would be to optimize pathing based on real-time road data, possible with Google Maps and Waze. The increased computation needed could be tackled through cloud computing (kubernetes, Spark).

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