Project Info

Winner

Fetch.ai: Smartest AI Agent Prize (Top 2 Teams - $3k Cash)

Healthiator

Devpost

This project did not submit a demo video on Devpost.

Inspiration

As a patient in the United States you do not know what costs you are facing when you receive treatment at a hospital or if your insurance plan covers the expenses. Patients are faced with unexpected bills and left with expensive copayments. In some instances patients would pay less if they cover the expenses out of pocket instead of using their insurance plan.

What it does

Healthiator provides patients with a comprehensive overview of medical procedures that they will need to undergo for their health condition and sums up the total costs of that treatment depending on which hospital they go-to, and if they pay the treatment out-of-pocket or through their insurance. This allows patients to choose the most cost-effective treatment and understand the medical expenses they are facing. A second feature healthiator provides is that once patients receive their actual hospital bill they can claim inaccuracies. Healthiator helps patients with billing disputes by leveraging AI to handle the process of negotiating fair pricing.

How we built it

We used a combination of Together.AI and Fetch.AI. We have several smart agents running in Fetch.AI each responsible for one of the features. For instance, we get the online and instant data from the hospitals (publicly available under the Good Faith act/law) about the prices and cash discounts using one agent and then use together.ai's API to integrate those information in the negotiation part. Ethics The reason is that although our end purpose is to help people get medical treatment by reducing the fear of surprise bills and actually making healthcare more affordable, we are aware that any wrong suggestions or otherwise violations of the user's privacy have significant consequences. Giving the user as much information as possible while keeping away from making clinical suggestions and false/hallucinated information was the most challenging part in our work.

Challenges we ran into

Finding actionable data from the hospitals was one of the most challenging parts as each hospital has their own format and assumptions and it was not straightforward at all how to integrate them all into a single database. Another challenge was making various APIs and third parties work together in time.

Accomplishments we're proud of

Solving a relevant social issue. Everyone we talked to has experienced the problem of not knowing the costs they're facing for different procedures at hospitals and if their insurance covers it. While it is an anxious process for everyone, this fact might prevent and delay a number of people from going to hospitals and getting the care that they urgently need. This might result in health conditions that could have had a better outcome if treated earlier.

What we learned

How to work with convex fetch.api and together.api.

What's next

As a next step, we want to set-up a database and take the medical costs directly from the files published by hospitals.

Analysis

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Technology

Found in codeClaimed only
  • PythonIn code
  • JavaScriptClaimed
  • ReactClaimed

1 of 3 appear in the indexed code. 2 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

7.3 KB

Source files

2

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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