CareLoop

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  CareLoop desgin

Inspiration

We study in a healthcare-related field, and during our daily coursework we constantly encounter the same problem: medical information is fragmented, overwhelming, and easily lost. Lab results, notes, and observations are often scattered across different systems or formats.

We wanted to create a solution that prevents important health information from getting lost and makes it easier to understand and use.

What it does

CareLoop prevents medical information from disappearing into disconnected systems.

It collects health data — lab results, clinical notes, voice entries, and observations — and brings everything into one structured, organized space. This is valuable for both patients and clinicians.

For patients, it creates understandable summaries and timelines. For clinicians, it generates structured SOAP notes and organized documentation.

CareLoop ensures that medical information remains accessible, structured, and traceable.

How we built it

We built CareLoop using: Large Language Models (LLMs) for summarization and structuring Python for backend logic and orchestration Microsoft Azure for cloud infrastructure and AI services

Challenges we ran into

One of the biggest challenges was aggregating medical data from different sources and formats. Health information often comes from multiple platforms, documents, and communication channels, each with its own structure. Another difficulty was maintaining consistency between outputs, especially when similar services or reports had different layouts depending on the provider. We also had to carefully design the prompt structure to minimize ambiguity and ensure reliable, medically coherent summaries.

Accomplishments that we're proud of

We are proud that: The system produces traceable and structured summaries Medical information becomes significantly easier to interpret The workflow is transparent and reviewable We successfully created a working prototype within a limited timeframe CareLoop demonstrates that AI can meaningfully support both patients and clinicians in organizing health data.

What we learned

We learned that healthcare systems often provide the same service in very different formats. Even when the medical content is similar, the presentation and structure can vary significantly. We also learned how powerful LLMs can be when properly guided with structured prompts and contextual rules. Most importantly, we gained a deeper understanding of how critical clarity and consistency are in medical workflows.

What's next for CareLoop

Next, we plan to: Improve accuracy and refinement of medical summaries Expand features and personalization options Integrate additional tools, such as a built-in genetic risk calculator Further optimize data structuring and traceability Our long-term goal is to develop CareLoop into a reliable, scalable health information assistant that supports real-world clinical workflows.

Built With

• .env
• ai
• azure
• css
• html
• javascript
• json
• llm
• openai
• python
• readme
• typescript