Transforming public health data with LLM & NLP
Transforming public health data (overview)
WinFully on Technologies is a leading healthcare IT consulting firm with 16 years of experience in interoperability and digital health strategies. In this case study, we delve into our collaboration with a startup company focused on developing an AI/ML-based tool for health index calculation. Let’s explore how we seamlessly integrated various components to create a robust solution.
Problem Statement
Our goal was to create a comprehensive health index system that could process large volumes of public health data from various sources, including TEFCA organizations. The data needed to be transformed, analyzed, and presented in a user-friendly format for healthcare professionals and patients.
Process
Data Ingestion and Translation:
- HL7 FHIR Format: We ingested public health data in HL7 FHIR format.
- AWS Cloud and Containerization: A containerized application on AWS translated the data efficiently.
Data Storage:
- Vector SQL Database: Transformed data found a home in a vector SQL database on the cloud.
- Scalability: The architecture ensures scalability as data volumes grow.
AI/ML Models:
- LLM Model: Leveraged for time-series analysis of health data.
- Transformer Model: Enhanced NLP capabilities for extracting insights.
Health Index Calculation:
- LLM and Transformer Integration: These models collaborated to calculate a comprehensive health index.
- Feature Engineering: Extracted relevant features from patient data.
Middleware and API Endpoint:
- Custom Middleware (JAVA): Responsible for data transformation and security.
- API Endpoint Creation: Secure endpoints exposed health index information.
User Interface:
- ReactJS Application: The UI displayed health indexes based on patients’ healthcare history.
Results
Our collaborative effort resulted in a powerful health index system that:
- Enhances Decision-Making: Healthcare professionals can quickly assess patient health using the calculated index.
- Empowers Patients: Patients gain insights into their overall health and can proactively manage their well-being.
- Complies with Standards: Our solution adheres to HL7 FHIR standards and TEFCA guidelines.
Technologies Used
- AWS Cloud: For data translation and storage.
- LLM and Transformer Models: AI/ML components for health index calculation.
- ReactJS: UI development.
- JAVA Middleware: Data transformation and API creation.
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