IQVIA

▌ Receipts

Metric

40%

Reduction in clinician research time

Metric

35%

Improvement in clinical-trial data accuracy

Metric

7%

Faster processing on large clinical datasets

Metric

Millions

Patient records supported

▌ What got built

1. § 01

   Led development of enterprise microservices on .NET Core 8 using Clean Architecture, CQRS with MediatR, and Domain-Driven Design — supporting millions of patient records across IQVIA's global platform.

2. § 02

   Engineered an AI-powered clinical decision support system using OpenAI GPT-4 and Azure OpenAI to deliver evidence-based treatment recommendations and drug-interaction warnings.

3. § 03

   Cut clinician research time by 40% via a RAG system with Pinecone vector databases for AI-driven search across millions of medical papers and clinical studies.

4. § 04

   Integrated Snowflake OLAP into the data warehouse, improving processing time on large clinical datasets by 7% while reducing AWS costs.

5. § 05

   Built an offline-capable iOS solution with DuckDB and SQLite for field researchers in low-connectivity areas.

6. § 06

   Improved clinical-trial data accuracy by 35% through an ML-based anomaly detection system that reduced manual review and strengthened regulatory compliance.

7. § 07

   Deployed containerised .NET services with Docker and Kubernetes on AWS EKS, with CI/CD via GitHub Actions.