Oncare

Inspiration

Cancer is a complex and challenging disease that affects millions of people around the world. Cancer patients face many physical and emotional difficulties during and after their treatment, such as pain, fatigue, nausea, anxiety, and depression. They also need to manage their medications, appointments, tests, and follow-ups. However, many cancer patients do not receive adequate primary care from their primary care physicians (PCPs), who are often unaware of their cancer history, treatment, and needs. This leads to poor coordination, communication, and quality of care for cancer patients.

What it does

Our solution is an app that leverages AI to provide comprehensive and personalized care for cancer patients.

How we build it

• Front-end: Using React Native to create a cross-platform mobile app that has a user-friendly and intuitive interface. MongoDB to store and manage the user’s data, such as their medical images, reports, and care plan.

• Back-end: Using Python and Flask to create a RESTful API that connects our app with our AI models. We can use existing AI model platforms or use PyTorch and TensorFlow to train a model using deep learning and machine learning techniques.

Possible challenges

• Data quality and availability: We need to ensure that the data we use to train and test our AI models are accurate, complete, representative, and up-to-date. We also need to comply with the ethical and legal standards for data collection, storage, and sharing, such as privacy, consent, and security.

• Model validation and evaluation: We need to ensure that our AI models are reliable, robust, and generalizable. We also need to measure and communicate the performance, limitations, and uncertainties of our AI models, such as accuracy, precision, recall, and confidence intervals.

• Integration and interoperability: We need to ensure that our AI models can be integrated and interoperable with the existing healthcare systems, workflows, and standards, such as electronic health records, clinical decision support, and health information exchange. We also need to ensure that our AI models can be updated and maintained over time.

• Adoption and acceptance: We need to ensure that our AI models can be adopted and accepted by the healthcare stakeholders, such as providers, patients, payers, and regulators. We also need to address the potential ethical, social, and legal implications of our AI models, such as bias, fairness, accountability, and transparency.

What we can Accomplish

Our app aims to:

• Empower cancer patients to take control of their health and well-being by providing them with a user-friendly interface that allows them to manage their treatment and needs on a daily basis.

• Create a personalized care plan for each patient based on their cancer type, stage, genomic profile, comorbidities, preferences, and goals.

• Connect patients with their PCPs and oncologists and facilitate the communication and coordination of care. The app will also provide patients with relevant and reliable information and education about their cancer and its management.

• Help patients cope with the physical and emotional challenges of cancer and its treatments. The app will assess the patient’s symptoms and vital signs and provide guidance and alerts.

What we learned

• Market research: We learned how to identify and validate the problems that we are trying to solve, as well as the potential customers and competitors that we are facing.

• Business model: We can learned how to define and communicate our value proposition. We also learned how to use tools such as the Business Model Canvas and the Lean Canvas to visualize and iterate on our business model.

• Pitching: We learned how to craft and deliver a compelling and concise pitch that captures the attention and interest of the judges. We also learned how to use storytelling, data, and visuals to support our pitch and showcase our solution.

What's next for Oncare

• Prototype: We will use the technologies and frameworks that we mentioned above to build a minimum viable product (MVP) of our app that demonstrates the core features and functionalities of our solution. We will also use some mock data and scenarios to test and evaluate our prototype and measure its performance and usability.

Built With

• adobe-creative-suite
• figma
• google-docs
• notion