Lack of medical compliance causes millions of dollars in deficit for the pharmaceutical industry and is detrimental to the health of the patient - Prescribe solves this concern using the power of the Internet of Things to make an intuitive, user centric experience that dispenses medications at the necessary times while recording valuable data for pharmacies.

What it does

Pharmacists operate a web based dashboard and add prescriptions to patient accounts as they receive them from patients' medical providers. An individual is able to see their own prescriptions, dosages, scheduled times, and other related health information using their mobile device which provides push notifications when recommended intake is scheduled. The user then interacts with our IoT dispenser in their home which holds the various medications for the entire family. Facial recognition identifies nearby users registered in the system, retrieves prescription information such as dosage, and dispenses accordingly. The pharmacist and patient are able to communicate with each other about important prescription announcements such a required pickup or prolonged non compliance and an analytics based database of industry medications are maintained for the pharmacies.

How we built it

The web app was created using Javascript, mobile app in Swift, facial recognition with Kinect in C#, and Arduino control in Python. Square and Twilio APIs were also employed.

Challenges we ran into

Creating a reliable hardware experience. Asynchronous software event handlers across multiple user-facing platforms all sharing a common database. Facial recognition in busy environments.

Accomplishments that we're proud of

We've built an end-to-end solution from the user facing software clients to physical hardware dispense to implement our solution for medical compliance!

What we learned

During this hackathon, we had the chance to explore many exciting APIs such as the Square for inventory management and Twilio for pharmacy to patient communication. This was fun to implement in addition to technologies we have already worked with such as Microsoft Cognitive Services in facial recognition with Kinect 2.0 and integration of web and mobile clients. We also had an interesting time constructing and refining the dispensing mechanism out of cardboard and a few servos.

What's next for Prescribe

We want to think about the challenges that pertain to scaling Prescribe for market use. We especially want to make the pharmacy facing dashboard as reliable and user friendly as possible while maintaining data latency and efficiency in practical functions. We would also like to use machine learning to optimize data analysis on prescription data to provide useful results for pharmacists.

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