Patient Data Pipeline for AI-Powered Diagnostics Platform

Challenge

The client's AI diagnostic models were underperforming because of inconsistent patient data flowing into the system. Medical records from multiple hospital systems arrived in different formats, with missing fields and duplicate entries. Data quality issues caused the AI to produce unreliable diagnostic suggestions, eroding physician trust in the platform.

Solution

We built a context engineering pipeline using MongoDB as the document store for flexible schema handling across hospital systems. Data refinement stages normalize, deduplicate, and enrich patient records before they reach the AI models. The entire pipeline runs on AWS with Datadog monitoring, ensuring HIPAA compliance at every stage with full audit trails.

Results