Transforming Public Health Data with LLM & NLP
We built a cloud-native AI/ML platform that standardizes large-scale public health data and generates continuously-updating health indices for clinicians and patients.
Results at a glance
The situation
A health-tech startup set out to turn massive, fragmented public health data into actionable health indices. Records arrived from diverse systems, including TEFCA-aligned networks, in inconsistent formats that resisted meaningful analysis. Without a common data model and near real-time processing, clinical insight stayed locked behind manual reconciliation. The market rewarded whoever could deliver compliant, trustworthy indicators at scale.
Design and deliver a cloud-native platform that ingests high-throughput public health data, standardizes it on HL7 FHIR, and applies advanced AI/ML to produce automated, continuously-updating health indices. The interfaces had to serve both clinicians and patients securely through APIs and dashboards.
How we delivered
FHIR standardization on containerized AWS services
Built HL7 FHIR translation pipelines running as containerized services on AWS to normalize data across TEFCA-aligned sources while preserving accuracy and compliance.
Horizontally scalable data store
Deployed a SQL database engineered for high-performance, horizontally scalable storage of both structured and semi-structured clinical data.
LLM and transformer AI/ML layer
Applied large language models and transformer architectures for NLP over clinical text and derivation of clinical indicators from heterogeneous inputs.
Dynamic Health-Index engine
Combined LLM outputs with transformer models in a scoring engine that recalculates health indices dynamically as new data continuously arrives.
Secure Java middleware and APIs
Orchestrated data flow through Java middleware enforcing strict security, exposing secure REST APIs for EHR integration and downstream consumers.
Personalized ReactJS analytics dashboard
Delivered a ReactJS dashboard surfacing personalized health indices, historical trends, and predictive insights with role-based access control.
The technical solution
A block-level view of the system we designed and delivered — data and control flowing across each stage.
What the users see
A wireframe of the experience we shipped — laid out for the people who use it every day.
AI that earns its place in production
We take models from data through training to deployment and monitoring, with governance and guardrails built in — not a pilot that never ships.
Governed data is the prerequisite for AI
Cataloged, quality-checked, access-controlled, and traceable — we build the data foundation that makes analytics and AI trustworthy and compliant.
Outcomes delivered
- Clinicians reached decisions faster as automated indices cut analysis time by roughly a third across common workflows.
- Patient engagement climbed as personalized indices and trends made health status legible and actionable.
- FHIR-based standardization drove consistently high interoperability across TEFCA-aligned data sources.
- The cloud-native architecture absorbed growing data volumes with a multi-fold increase in scalability headroom.
- Secure APIs and role-based access let the platform integrate cleanly with existing EHR environments without compromising compliance.
Technology stack
The platforms, frameworks, and standards behind the solution.
Cloud & Infrastructure
AI/ML
Interoperability
Backend & Data
Frontend
Want results like these?
Tell us the outcome you're after. We'll scope a short, fixed-fee discovery and a plan to get there.
Ready to start your digital transformation?
Let's talk about your roadmap, your compliance needs, and where technology can move your business forward.