Primary Track β Best Healthcare Hack βοΈβ¨
UPDATE [14.02.23] β Something special is cooking! Check updates for more.
Inspiration π‘
Healthcare is one of the most important and critical industries in the world. Providing quality medical care to patients is essential, but it is often hindered by various challenges such as overburdened healthcare workers, lack of medical devices in rural areas, and administrative stress. With the advent of artificial intelligence and machine learning, the healthcare industry has a unique opportunity to tackle these challenges head-on and revolutionize the way medical care is delivered.
With this as context, we plan to tackle the Provider Shortage & Burnout and Access to Care strategic themes.
What it does π€
HealthifAI aims to tackle several key pain points in the healthcare industry - specifically for the following :
Provider Shortage & Burnout :
- Intuitive, easy & safe digital patient record entry which eliminates the need for manual and legacy record entry methods.
- We provide an ML-powered "soft diagnosis" to save time for doctors and nurses.
- We have location-based COVID-19 alerts to better equip workers.
- Multilingual speech-to-text notes, because it's easier!
- Reminder system to help with medication/check-ups. Keeping track of everything is hard!
Access to care :
- Multilingual communication model that transcribes speech from any language into English. This is particularly helpful in rural areas where communication is a barrier.
- Experimental Computer-Vision powered heart rate monitor. This transforms everyday hand-held devices into medical devices - an exciting vision for the future!
How we built it βοΈ
HealthifAI was built using cutting-edge AI and machine learning technologies, including Open-AI and Flask. The team used the Flask framework to build a RESTful API that can handle incoming requests and return appropriate responses. We used React.js & Tailwind as CSS framework. The Authentication (OAuth) has been done by Firebase & weβre also using the Cloudstore database for storing user logs.
The API was integrated with the Open-AI speech-to-text model "whisper" to transcribe speech from any language into English. Further, Gaussian Naive Bayes for classification was implemented to "soft diagnose" patients based on their symptoms.
The vision-powered heart rate monitor was built using image processing techniques built with OpenCV. In essence, the camera detects sensitive changes in the neck and forehead which is then used to infer heart rate.
Design π¨
We were heavily inspired by the revised version of Double Diamond design process, which not only includes visual design, but a full-fledged research cycle in which you must discover and define your problem before tackling your solution & then finally deploy it.
- Discover: a deep dive into the problem we are trying to solve.
- Define: synthesizing the information from the discovery phase into a problem
- Develop: think up solutions to the problem.
- Deliver: pick the best solution and build that.
Research π
- What do healthcare workers spend most time on? | NIH
- Measuring Heart-rate through muted videos | MIT Media lab
- An overview of healthcare in rural areas | Rural Health Information
- Provider Burnout | NIH
- Communication in rural healthcare | Optimizing rural healthcare
- Can we use ML to diagnose diseases? | NIH
- Lack of medical workers plagues developing world | Reuters
Best Domain Name from Domain.com β
Our domain is registered at healthifai-with.tech. Why have clunky sentences when your product motto can be your website name! :)
Best use of Google Cloud βοΈ
- Firebase : We used it for authentication purposes - application safety is pivotal!
- Google Colab : We used it to test and train our models. It helped in quick line-by-line execution of dataframe manipulation and model output.
- Google Cloud : GCE & GCP to host our server. Speedy inferences and easy integration were the reasons for this choice :)
Challenges we ran into π€
Building HealthifAI was not without its challenges. One of our challenges was integrating the various AI and machine learning technologies into a cohesive and functional system. This required a deep understanding of each technology, as well as expertise in data processing and software engineering. We participated in hourly review sessions to share findings of distributed research - our biggest challenge was sticking to tight schedules!
Accomplishments that we're proud of β¨
We have tried to tackle multiple problems - we are happy to say that we were able to come up with solutions for most. We're proud to say that we have tried to bring a difference to the field of healthcare - something we deeply care about. Also, we were able to accomplish the complete integration of bleeding-edge technologies. Last, but not least - we are proud of our team's hard work and dedication in bringing HealthifAI to life.
What we learned π
The development of HealthifAI has been a journey of learning and discovery. We were exposed to the multiple challenges faced by healthcare workers as well - they are a cornerstone for public health, and keeping them stress-free can improve the lives of both patients and providers.
Our team has worked tirelessly to bring forth solutions to the aforementioned healthcare challenges. Most importantly, we've learned the importance of perseverance and collaboration in bringing a vision to life.
What's next for HealthifAI π
The sky's the limit for HealthifAI. Our team is already exploring new ways to improve and expand the platform, including incorporating new technologies and partnering with healthcare providers to bring our vision to a wider audience. We're committed to making a real impact in the healthcare industry and changing lives for the better.
That's it for now! We can't wait to see the impact that HealthifAI will have on the world. Stay tuned for updates and more exciting developments!
![Alexiana "Lexi" Jimenez](https://media.licdn.com/dms/image/C4E03AQHgS1EwNsLroQ/profile-displayphoto-shrink_800_800/0/1612033784971?e=1681344000&t=pKuLjZjQxSHDcNiWqzRJsEm56u4eljQ_l2UgHi0_dvw&type=square&v=beta "Alexiana "Lexi" Jimenez")
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