## Reimagining Patient Education and Treatment Delivery through Gamification

Imagine walking into a doctors office to find out you’ve been diagnosed with a chronic illness. All of a sudden, you have a slew of diverse healthcare appointments, ongoing medication or lifestyle adjustments, lots of education about the condition and more. While in the clinic/hospital, you can at least ask the doctor questions and try to make sense of your condition & management plan. But once you leave to go home, **you’re left largely on your own**.

We found that there is a significant disconnect between physicians and patients after patients are discharged and diagnosed with a particular condition. Physicians will hand patients a piece of paper with suggested items to follow as part of a "treatment plan". But after this diagnosis meeting, it is hard for the physicians to keep up-to-date with their patients on the progress of the plan. The result? Not surprisingly, patients **quickly fall off and don’t adhere** to their treatment plans, costing the healthcare system **upwards of $300 billion** as they get readmitted due to worsening conditions that may have been prevented.

But it doesn’t have to be that way…

We're building an engaging end-to-end experience for patients managing chronic conditions, starting with one of the most prevalent ones - diabetes. **More than 100 million U.S. adults are now living with diabetes or prediabetes**

## How does Glucose Guardian Work?

Glucose Guardian is a scalable way to gamify education for chronic conditions using an existing clinical technique called “teachback” (see here- [link](https://patientengagementhit.com/features/developing-patient-teach-back-to-improve-patient-education)). We plan to partner with clinics and organizations, scrape their existing websites/documents where they house all their information about the chronic condition, and instantly convert that into short (up to 2 min) voice modules.

Glucose Guardian users can complete these short, guided, voice-based modules that teach and validate their understanding of their medical condition. Participation and correctness earn points which go towards real-life rewards for which we plan to partner with rewards organizations/corporate programs.

Glucose Guardian users can also go to the app to enter their progress on various aspects of their personalized treatment plan. Their activity on this part of the app is also incentive-driven.

This is inspired by current non-health solutions our team has had experience with using very low barrier audio-driven games that have been proven to drive user engagement through the roof.

## How we built it

We've simplified how we can use gamification to transform patient education & treatment adherence by making it more digestible and fun. We ran through some design thinking sessions to work out how we could create a solution that wouldn’t simply look great but could be implemented clinically and be HIPAA compliant.

We then built Glucose Guardian as a native iOS application using Swift. Behind the scenes, we use Python toolkits to perform some of our text matching for patient education modules, and we utilize AWS for infrastructure needs.

## Challenges we ran into

It was difficult to navigate the pre-existing market of patient adherence apps and create a solution that was unique and adaptable to clinical workflow. To tackle this, we dedicated ample time to step through user journeys - patients, physicians and allied health professionals. Through this strategy, we identified education as our focus because it is critical to treatment adherence and a patient-centric solution.

## We're proud of this

We've built something that has the potential to fulfill a large unmet need in the healthcare space, and we're excited to see how the app is received by beta testers, healthcare partners, and corporate wellness organizations.

## Learning Points

Glucose Guardian has given our cross-disciplined team the chance to learn more about the intersection of software + healthcare. Through developing speech-to-text features, designing UIs, scraping data, and walking through patient journeys, we've maximized our time to learn as much as possible in order to deliver the biggest impact.

## Looking Ahead

As per the namesake, so far we've implemented one use case (diabetes) but are planning to expand to many other diseases. We'd also like to continue building other flows beyond patient education. This includes components such as the gamified digital treatment plan which can utilize existing data from wearables and wellness apps to provide a consolidated view on the patient's post-discharge health.
Beyond that, we also see potential for our platform to serve as a treasure trove of data for clinical research and medical training. We're excited to keep building and keep creating more impact.
## Inspiration

Our inspiration for this project was the technological and communication gap between healthcare professionals and patients, restricted access to both one’s own health data and physicians, misdiagnosis due to lack of historical information, as well as rising demand in distance-healthcare due to the lack of physicians in rural areas and increasing patient medical home practices. Time is of the essence in the field of medicine, and we hope to save time, energy, money and empower self-care for both healthcare professionals and patients by automating standard vitals measurement, providing simple data visualization and communication channel.

## What it does

What eVital does is that it gets up-to-date daily data about our vitals from wearable technology and mobile health and sends that data to our family doctors, practitioners or caregivers so that they can monitor our health. eVital also allows for seamless communication and monitoring by allowing doctors to assign tasks and prescriptions and to monitor these through the app.

## How we built it

We built the app on iOS using data from the health kit API which leverages data from apple watch and the health app. The languages and technologies that we used to create this are MongoDB Atlas, React Native, Node.js, Azure, Tensor Flow, and Python (for a bit of Machine Learning).

## Challenges we ran into

The challenges we ran into are the following:

1) We had difficulty narrowing down the scope of our idea due to constraints like data-privacy laws, and the vast possibilities of the healthcare field.

2) Deploying using Azure

3) Having to use Vanilla React Native installation

## Accomplishments that we're proud of

We are very proud of the fact that we were able to bring our vision to life, even though in hindsight the scope of our project is very large. We are really happy with how much work we were able to complete given the scope and the time that we have. We are also proud that our idea is not only cool but it actually solves a real-life problem that we can work on in the long-term.

## What we learned

We learned how to manage time (or how to do it better next time). We learned a lot about the health care industry and what are the missing gaps in terms of pain points and possible technological intervention. We learned how to improve our cross-functional teamwork, since we are a team of 1 Designer, 1 Product Manager, 1 Back-End developer, 1 Front-End developer, and 1 Machine Learning Specialist.

## What's next for eVital

Our next steps are the following:

1) We want to be able to implement real-time updates for both doctors and patients.

2) We want to be able to integrate machine learning into the app for automated medical alerts.

3) Add more data visualization and data analytics.

4) Adding a functional log-in

5) Adding functionality for different user types aside from doctors and patients. (caregivers, parents etc)

6) We want to put push notifications for patients' tasks for better monitoring.
## Inspiration

Rates of patient nonadherence to therapies average around 50%, particularly among those with chronic diseases. One of my closest friends has Crohn's disease, and I wanted to create something that would help with the challenges of managing a chronic illness. I built this app to provide an on-demand, supportive system for patients to manage their symptoms and find a sense of community.

## What it does

The app allows users to have on-demand check-ins with a chatbot. The chatbot provides fast inference, classifies actions and information related to the patient's condition, and flags when the patient’s health metrics fall below certain thresholds. The app also offers a community aspect, enabling users to connect with others who have chronic illnesses, helping to reduce the feelings of isolation.

## How we built it

We used Cerebras for the chatbot to ensure fast and efficient inference. The chatbot is integrated into the app for real-time check-ins. Roboflow was used for image processing and emotion detection, which aids in assessing patient well-being through facial recognition. We also used Next.js as the framework for building the app, with additional integrations for real-time community features.

## Challenges we ran into

One of the main challenges was ensuring the chatbot could provide real-time, accurate classifications and flagging low patient metrics in a timely manner. Managing the emotional detection accuracy using Roboflow's emotion model was also complex. Additionally, creating a supportive community environment without overwhelming the user with too much data posed a UX challenge.

## Accomplishments that we're proud of

✅deployed on defang
✅integrated roboflow
✅integrated cerebras

We’re proud of the fast inference times with the chatbot, ensuring that users get near-instant responses. We also managed to integrate an emotion detection feature that accurately tracks patient well-being. Finally, we’ve built a community aspect that feels genuine and supportive, which was crucial to the app's success.

## What we learned

We learned a lot about balancing fast inference with accuracy, especially when dealing with healthcare data and emotionally sensitive situations. The importance of providing users with a supportive, not overwhelming, environment was also a major takeaway.

## What's next for Muni

Next, we aim to improve the accuracy of the metrics classification, expand the community features to include more resources, and integrate personalized treatment plans with healthcare providers. We also want to enhance the emotion detection model for more nuanced assessments of patients' well-being.