Health Sherpa

Inspiration

In Germany every employee spend in average 19,5 days of sick leave per year. The current healthcare systems all over the world are focusing mainly on sick care instead of sickness prevention. Fitness applications does a very good job visualizing your personal data, but do not suggest any preventive measures or recommendations neither they combine this data with known information about weather, pollution, nutrition and healthcare hazards. Its current goal is not to cure serious diseases but to keep the user healthy.

What it does

We want to connect the dataset provided by different health IOT devices and consolidate this on our platform to perform recommendation based on our learned models. Our recommendation engine takes into account the data, location based analytics and medical history into account.

How I built it

We use AI based personal assistant to relay the recommendation in a meaningful conversation. The assistant's recommendation varies from

Taking an immunity booster.
Wearing a wind jacket. -Taking a power nap in the afternoon.

Challenges I ran into

The data collection can be a challenge specifically in Europe, which is under the GDPR consumer data protection law. However we rely on the api provided by Thryve health for database access of consumer. Also the user experience can be considered a challenge - how to make the product engaging and make it to a daily habit

Accomplishments that I'm proud of

We accomplished to identify the inefficiency in self diagnosis and work together in a multicultural group to find a promising solution.

What I learned

We learned about how Machine learning can help in self diagnosis which is typically done using a couple of google search or from friends and families. The learned models can evolve over time to give better recommendations, helping in preventing improper treatment.

What's next for Health Sherpa

We would partner with secure customer data provider institution, We extend our solution to other home IOT devices, like smart mirror with inbuilt Alexa to give recommendations. We can use more data like nutrition, medication, training routine to further personalize the experience