Inspiration
Inspired by internships at Hconnect where they help translate data to two different systems. We applied it to a broader problem nationwide, specifically healthcare, where we use AI to use scattered data to save people's lives.
What it does
Translates data from one format to the other so that the legacy hospital system can read the data directly without the need to get a new system
How we built it
Using experience from working in Hconnect to translate data and make sense of it, I was able to do it here too. Assisted by AI we researched online, came up with APIs which helps us translate data from whoop to the hospital system.
Challenges we ran into
Connecting all 3 different parts of the entire workflow together.
Accomplishments that we're proud of
We are proud of the solution we came up with together to tackle a serious problem.
What we learned
We learned a lot about the health system, such as the data health specialists need. We also learned that there is a lot of data in the world and it's hard to parse through it to get value out of it.
What's next for HealthConnect
The next step is acquiring more data to be able to identify risks/ diseases months before it actually happens as opposed to the shorter time period we have now
Built With
- apple-healthkit
- cds-hooks
- claude
- css
- fhir-r4
- github
- google-fonts
- hl7-fhir
- html5
- javascript
- json
- loinc
- oauth2
- oura-api
- playwright
- rest-api
- smart-on-fhir
- snomed-ct
- svg
- ucum
- whoop-api