Case study
Personalized Health, Powered by AI
Stack
DURATION
TEAM SIZE
Key Results & Value:
- Patient Outcome Improvement: The platform drove a 30% increase in patient adherence to prescribed preventive care plans, resulting in healthier populations.
- Cost & Clinical Impact: By delivering highly personalized preventive strategies, the client observed a 10% reduction in preventable hospital visits, resulting in significant cost savings and improved patient outcomes.
- Clinical Accuracy & Efficiency: The integration of vast patient data (EHR, wearables, genetic) with deep learning models increased risk checks by 20% while boosting physician efficiency by 15% through clearer, data-backed information.
- Security & Data Integrity: The complex system, built across Python/React.js and cloud platforms, achieved 100% HIPAA compliance through robust encryption (TLS, AES-256) and role-based access control (RBAC), securing sensitive data.
Our Client:
FastAPI
OpenAI GPT-4
LangChain
ChromaDB
pyTesseract
Azure
Stack:
- Programming Languages: Python, TypeScript
- AI/ML Frameworks: TensorFlow, PyTorch, Scikit-learn
- Data Processing: Apache Kafka, Apache Spark
- Database/Data Warehousing: PostgreSQL, Snowflake, Amazon Redshift
- Cloud Platform: AWS (S3, EC2, RDS), Google Cloud Platform (GCS)
- Containerization & Orchestration: Docker, Kubernetes
- Web Frameworks: React.js, Flask, Django
- Integration Standards: HL7 FHIR
- Security: OAuth2, RBAC, Encryption (TLS, AES-256)
- Monitoring & Logging: Prometheus, Grafana, ELK Stack
The Problem
They came to us with a familiar, yet challenging, problem: they had vast amounts of patient information, but it was scattered across different systems and not being utilized effectively. Their usual methods of care, while suitable for most people, couldn’t provide the detailed insights required for truly personalized medicine.
They needed a strong, flexible system that could consolidate all kinds of data, run intelligent AI tests on it, and keep all that sensitive patient information completely secure. Their main goal? To provide highly personalized treatment and preventive plans, which would significantly improve the health of patients and the smoothness of their care.
The Solution
Our team built an advanced AI-powered Personalized Healthcare Recommendations Platform. It’s a knowledgeable health guide that continually learns and adapts to each patient’s needs.
We have developed a comprehensive system that leverages extensive patient data, including electronic health records (EHRs), real-time information on daily life, and detailed genetic data, to deliver clear, highly personalized healthcare advice.
What It Does & How It Works
We built our solution with top-notch technology, focusing on what modern healthcare truly needs:
Pulls All Your Health Information Together:
It smoothly gathers data from EHR systems (using HL7 FHIR), wearable devices (such as smartwatches), and even genetic testing platforms. We used Apache Kafka to handle incoming data at a high rate and Apache Spark to process it efficiently.
Smart AI Brain:
Smart AI Brain: This is where the powerful AI comes in. Our team used advanced machine learning models (like TensorFlow, PyTorch), including deep neural networks. We also utilized Natural Language Processing (NLP) to analyze notes from doctors and specialized tools for genetic data. For reliable growth, we used Docker and Kubernetes.
Your Health Plan:
The system creates tailored plans for preventive care, specific treatment steps, and helpful lifestyle changes, all based on each person’s health risks. It also includes proven medical advice and tools to help doctors make decisions.
Keeps Your Data Super Safe:
We ensured that patient data is incredibly secure. We built encryption to comply with HIPAA rules. We also implemented role-based access control (RBAC), ensuring that only authorized personnel can view specific data and conduct regular security checks. We store data securely using AWS S3 and Google Cloud Storage.
Easy for Doctors to Use:
We created a user-friendly, fast website (built with React.js) for healthcare providers. This allows them to view all the personalized advice easily. The backend, which handles a significant amount of work, was built using Python (Specifically, Flask and Django).
Mike Fliorko
Geschäftsführender Direktor, EMEA
Michael Babylon
Sales Director, Europe
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