E-Health- Health Tracking App

The Application's Logical Arch Diagram

Inspiration

In today’s fast paced modern world disturbed by Pandemic, taking care of the wellbeing of the elderly people has always got a prime importance but often challenged with different difficulties. Shattered by social distancing, lockdown, financial debacle, the wellbeing for every human from a teenager to septuagenarian is at stake. This acute problem direly needs some meaningful AI driven software solution strategizing the caregiving with a blend of empathy towards the clients.

What it does

E-Health simplifies the routine health tracking process through a streamlined AI driven technology.

A care agent (Care Provider/ Agency) can schedule the threshold values for all the health tracking factors like Sleep, Activity, Blood Glucose level, Blood Pressure etc.

This set up will be specific to each of the care candidates’ health profile (Care Receivers) who will subscribe for this app. The Care Candidate will need to install some specific apps (including Google FIT) in the wearable health device.

There is another persona named, Care Sponsor who basically sponsor this solution for his/her relative (Care Receiver). The care sponsor will be able to see comparative data analytics and receive real time alerts based on the care candidate’s profile.

The PEGA solution will integrate with Google FIT API and fetch all the health data for comparison with the threshold values set by Care Agents. This solution can work seamlessly with any health device which has just the Google Fitness App installed.

How we built it

We used simple PEGA workflow for two work types 
Set up the threshold limit for health factors (Actor will be Care Agents) – The care agents can use this case type to set up the threshold limit for any health factors like Sleep, Activity, Blood Glucose Level etc. pertaining to each care candidate.
Fetch to see the health data (Actors will be Care Sponsors) – The care sponsors can use this case type to check the health info pertaining to his/her candidate for any time frame.
We used the standard dashboard features to show up the data for both the care agents and care sponsors.
The Google FIT API can be consumed in PEGA and REST-Connectors can be invoked to fetch the results for automation and analytics.

Challenges we ran into

Establishing the connection between Google Fitness API and PEGA through REST was challenging. It needed more research and development time. For this reason, the application is built on stubbed data. But the data element is used keeping the parity with fitness API so that, the solution should work seamlessly once the integration is done. For time as a constraint, we were able to complete the application for one health factor i.e., Sleep.

Accomplishments that we're proud of

Within a very short span we made a tiny prototype solution which can be showcased to magnify the possibilities of the idea we have come up with.
The idea we have conceptualized is unique and it can be a grand success if this can be built as a full-blown solution.

What we learned

The REST API integration with Google Fitness gave us lot of learning scope in the architectural aspects of integrations. We are still solving the problem.

What's next for E-Health- Health Tracking App

The road map for the E-Health lies ahead like below –

The Integration with Google Fitness API will be established.
The app will be extended for all other health factors like Activity Steps, Blood Pressure, Blood Glucose level etc.
Alexa Integration will be established so that care sponsors can use ALEXA’s IOT features to check the health data of the certain candidate through the case type already available.