Data normalization lessons from multi-source healthcare integrations
The messiest part of healthcare integration work is often not transport. It is deciding how the receiving system should interpret inconsistent source data.
Points worth discussing:
• Terminology and code system mismatches
• Required field assumptions that vary by system
• Error handling that is understandable to non-engineering teams
What normalization rule ended up being far more important than expected?