## Inspiration

CareGuide was developed by our team in response to the pressing problem of medical errors in healthcare. A Johns Hopkins study found that medical errors account for over 250,000 deaths each year in the US, making them the third most common cause of mortality. Incomplete patient information and poor communication are frequent causes of these mistakes. Furthermore, 42% of patients experience anxiety when it comes to taking care of their health at home, underscoring the need for improved support networks. Our goal was to develop a solution that would lower these risks and enhance patient outcomes by streamlining communication and offering thorough health overviews.

## What it does

CareGuide is an AI-powered mobile app that enhances home healthcare by providing post diagnosis patient care. We help patients based on the interactions between patients, nurses, and doctors while also integrating the information from the discharge notes and Electronic Health Records (EHRs) of the patients. We are tailoring personalized care plans to individual patient needs based on their data and AI models which were specifically trained for this job. This combination of features ensures that patients receive timely, accurate, and personalized care, ultimately improving their overall health experience.

## How we built it

Our application development process employed React.js for a user-friendly front-end interface, complemented by extensive CSS styling. The back-end infrastructure was constructed using Python with Flask serving as the server framework. We incorporated advanced speech-to-speech technology and utilized Retrieval-Augmented Generation (RAG) to enable Large Language Models (LLMs) to access and interpret patients' clinical notes and doctor-patient conversations. Our cloud infrastructure, secured with AWS Client-Side Encryption, ensures the secure storage and processing of data, thereby upholding stringent data privacy standards and complying with HIPAA healthcare regulations.

## Challenges we ran into

During the hackathon, we encountered significant technical challenges, particularly with setting up local AI using Intel AI on the provided hardware to create a medical specific model. Ensuring patient data privacy through local AI is critical, and regulatory compliance demands rigorous testing and implementation of robust security measures, which is difficult to achieve under tight deadlines. Consequently, we pivoted to alternative technologies to ensure functionality. Prioritizing features that address the actual needs of healthcare professionals was also challenging. However, we successfully conducted interviews with five doctors, gathering valuable feedback that helped us refine our product to better meet their needs.

## Accomplishments that we're proud of

We are proud of the positive feedback received during the testing phase, which validated our approach and highlighted the potential impact of CareGuide. Successfully improving care received by patients with anyone around them and it allowed family members to focus more on patient health. Furthermore, providing real-time support and personalized care significantly improved patient satisfaction, demonstrating the effectiveness of our solution. We also cold called doctors and got validation from 5 of them for the product and even had positive feedback from fellow hackers.

## What we learned

Participating in this hackathon taught us the importance of user-centered design, as engaging with end-users throughout the development process was crucial in creating a practical and effective solution. We also learned the value of building a scalable app that can grow with the increasing demand for home healthcare. Additionally, the experience highlighted the importance of effective team collaboration and incorporating diverse feedback to enhance our project.

## What's next for CareGuide

Looking ahead, we plan to expand CareGuide’s features by adding very strict data privacy laws to avoid any HIPPA compliant issues. We also want to improve the model efficiency for medical information and keep training it on further datasets. We will also start monitoring of vital signs to help patients avoid any upcoming health complication. We aim to scale up the deployment to more regions and healthcare systems to reach a broader audience. Continuous improvement based on user feedback and technological advancements will ensure that CareGuide remains at the forefront of home healthcare innovation, providing the best possible care for patients.

## References

1. Centers for Disease Control and Prevention, National Center for Health Statistics. About Multiple Cause of Death, 1999–2020. CDC WONDER Online Database website. Atlanta, GA: Centers for Disease Control and Prevention; 2022. Accessed February 21, 2022.
2. National Safety Council analysis of Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics System, Mortality 2018-2022 on CDC WONDER Online Database, released in 2024. Data are from the Multiple Cause of Death Files, 2018-2022, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at <http://wonder.cdc.gov/mcd-icd10-expanded.html>
3. Brady RE, Braz AN. Challenging Interactions Between Patients With Severe Health Anxiety and the Healthcare System: A Qualitative Investigation. J Prim Care Community Health. 2023 Jan-Dec;14:21501319231214876. doi: 10.1177/21501319231214876. PMID: 38041442; PMCID: PMC10693786.
## Inspiration

Eager to begin coding, I opened the Princeton Hackathon's website, and soon found myself wasting several minutes trying to find details regarding the event. There was no information about when workshops were being held, when meals were, where submissions went, etc... It was then that a friend told me to go to hackprinceton.com/**guide** and hackprinceton.com/**dayof** for this **critical** information. Therefore, I vowed that one day others would be spared this pain. Thus, I created a chrome extension that adds the \_ dayof \_ and \_ guide \_ links to the hackprinceton website.

## What it does

That's right! The Hack Princeton website is allowed to reach its full potential with \_ dayof \_ and \_ guide \_ links using this nifty extension.

## How I built it

I googled "how to make a google chrome extension" and followed the results I found. Stackoverflow was a godsend, and I couldn't have done this project without it. I created the necessary manifest.json file along with the interacting javascript and html files to accomplish this.

## Challenges I ran into

N/A

## Accomplishments that I'm proud of

The Hack Princeton website is now complete thanks to this invention.

## What I learned

I learned how to make chrome extensions.

## What's next for Hackathon [Fixed]

I've considered just recommending that next year's staff includes such vital information on the homepage/toolbar without making students type new urls in.
## Inspiration

Our inspiration for creating this AI project stemmed from the desire to leverage current technology to enhance the lives of our community members, particularly the elderly. Recognizing the difficulties faced by aging individuals, including our own grandparents, to embrace technological advancements, and their concerns about health conditions, we sought a way to integrate technology and healthcare services seamlessly into their lives. Thus, SageWell was conceived—an AI companion designed to provide personalized support to seniors by navigating medical information alongside their healthcare providers. At SageWell, we believe in accessibility of AI for all age groups.

## What it does

SageWell enables users to seek medical information through natural language interactions, allowing elderly individuals to pose questions verbally and receive spoken responses, mimicking human conversation.

## How we built it

SageWell leverages the capabilities of two Monster APIs: OpenAI-Whisper Large-v2 for speech-to-text conversion and Meta's llama-2-7b-chat-hf for refining our reinforcement learning model. Our model was trained on MedQuAD, a comprehensive dataset containing over 47,000 medical question-answer pairs, Drugs, Side Effects and Medical Condition dataset, and Drugs Related to Medical Conditions dataset. The frontend of the web app was built using React, a JavaScript library, and the service we integrated into our app to store all the audio files was Firebase.

## Challenges we ran into

In the process of developing SageWell, we encountered several challenges. Since there were many integrations in our application, from speech-to-text transcription and MonsterAPIs for fine-tuned LLMs, to storage service providers, we faced difficulties trying to link all the individual pieces together. We learned several new tools and technologies and also spent time fine-tuning models to provide medical information. Additionally, we encountered setbacks when we were finishing up our project as we were facing CORS errors when making API calls from the browser - we were able to work around this by adding a proxy, which served as a bridge between our client and server.

## Accomplishments that we're proud of

We empowered our primary user demographic, the elderly, to engage with SageWell through voice interactions instead of having to type text. We addressed the challenges that many seniors encounter when typing on digital devices, thereby increasing the accessibility of the process of seeking medical information and ensuring inclusivity in AI for all age groups, especially the elderly.

Our reinforcement learning model has demonstrated effectiveness, evidenced by the decrease in training loss after each iteration and to 0.756 at the end. This indicates that the model fits well on the training data.

We merged three datasets: the Medical Question Answering data set, the Drugs, Side Effects and Medical Condition data set, and the Drugs Related to Medical Conditions data set. By training our model for SageWell on these datasets obtained from NIH websites and drugs.com, we allowed SageWell to provide medical information to users based on reliable, comprehensive databases.

## What we learned

Through this project, we gained valuable insights into the startup ecosystem and developer space. We expanded our skill set by refining our model, working with new APIs and storage providers, and creating a solution that addresses the specific challenges faced by our target audience.

## What's next for SageWell

The journey of SageWell is just beginning. Moving forward, we aim to expand its capabilities to assist in additional areas crucial for the well-being of the elderly, such as medication reminders and guidance on accessing support for domestic chores. Furthermore, we envision integrating features that facilitate connections between seniors and younger generations, including their grandchildren and other youth in their communities. By fostering these intergenerational connections, SageWell will expand its targeting market size by not only keeping the elderly engaged with their loved ones but also ensuring they remain connected to the evolving world around them. Through SageWell, we look forward to continuing to push for accessibility of AI for all age groups.