MediQuery AI
Natural Language Analytics for Healthcare
Built an AI-powered healthcare analytics platform enabling natural language querying of hospital alert data with real-time insights.
Business Problem
Clinical leaders often lack the technical skills to query complex analytical databases, creating a bottleneck where IT teams are constantly fielding simple data requests for alert trends.
Technical Challenges
Mapping natural language to complex analytical schemas (ClickHouse), ensuring SQL generation accuracy for healthcare metrics, and processing high-volume alert streams with low latency.
Architecture
The system uses a 'Semantic Layer' to represent hospital alert data. LangChain orchestrates the interaction between the user query and the Groq LLM to generate ClickHouse-compatible SQL. Data is stored in MinIO as Parquet and queried via ClickHouse's S3 engine.
Implementation
I led the development of the 'Text-to-SQL' engine, implementing few-shot prompting to improve medical terminology understanding and building a real-time dashboard with Recharts.
Scalability
By using ClickHouse and MinIO, the platform can scale to handle massive volumes of alert data with sub-second query times.
Results / Impact
Dramatically reduced the time-to-insight for clinical managers and improved the identification of high-risk patients through automated natural language analytics.
Lessons Learned
Prompt engineering for analytics is as much about data context as it is about language. Building a robust semantic layer for ClickHouse is critical for LLM accuracy.
Interested in the technical implementation?
Let's discuss how this architecture can be applied to your specific healthcare challenges.