Collect (Smart Wearable)
Doctor/Patient Login (Deliver)
What the doctor sees (Deliver)
Doctor sends patient messages (Communicate)
Many people do not have access to basic healthcare due to lack of money, transportation or resources. The government allocates a large sum of money to companies which provide and manage the free healthcare that is provided to these people. If a person is rated as "high risk", the company currently invests a lot of money in a personalized preventative care plan, which tries to save money in the long run by taking early steps to treat the problem.
What it does
We have developed a managed care solution that uses big data technologies to proactively prevent medical emergencies. We work around data in these 4 steps: Collect -> Analyze -> Deliver -> Communicate The best part about the process is that it is a cycle, in that after we communicate the data, we need to go back and collect more data and the whole process starts over. This shows that our solution is scalable, sustainable and self-sufficient.
How we built it
- Collect: We designed our very own smart wearable device using an arduino, intel edison, GPS, some sensors and a Wi-Fi shield.
- Analyze: We developed a big data algorithm to analyze the data. To do this, we wrote a data processing algorithm which finds patterns in the database using Python.
- Deliver: We developed a web app where the doctor will be able to log in and see the patient's data. This was built using Ruby on Rails to write the web app dashboard, bootstrap to design the front end and mySQL to handle the database.
- Communicate: The doctor will be able to use the same web app he is already logged into, to securely send messages to the patient and receive replies. This was built using node.js for the chat app within the web app.
Challenges we ran into
- Since we are working with very personal patient data, in order to meet important government regulations (such as HIPAA), all the data this is transferred throughout this process will be encrypted.
- Only raw data will be used during the analysis step, when the algorithm examines the data to make the predictive model.
Accomplishments that we're proud of
Providing an end-to-end solution for the under-privileged, less educated & elderly (people who do not have any experience with technologies). This means we had to ensure that the overall process is minimal, does not require the patient to do anything and is easy for anyone to use.
What we learned
- The overall healthcare business model and how important it is to have early detection technologies readily available to the general population.
- An incentive to the population is that they periodically get a free partial checkup using an easy-to-use device.
- An incentive to medical professionals is it saves time by allowing them to diagnose patients online
What's next for Project BigDoc
- Work with the government to distribute the smart wearable to these high risk patients, all over the country, for free.
- Target healthcare enterprises who are spending a lot of money on preventative care and show them our complete packaged solution.
- Reach out to medical professionals to get them on board our web app through which they can view the data and communicate effectively with their patients.
- Add different kinds of sensors to get more data that will be used to make an even stronger database, more accurate trend charts and a better predictive model.