Refactor Health
We helped build Refactor Health's common data-model system — turning messy health data into clean, standardized foundations for ML and AI.
- Industry
- Health Technology
- Engagement
- Data Engineering · AI Infrastructure
Health data is famously messy: every organization records the same clinical reality in different formats, different codes, different systems. Machine learning can’t learn from chaos — and in healthcare, the cost of dirty data isn’t a bad dashboard, it’s a bad model.
Agilevent helped Refactor Health build out its common data-model system — the standardization layer that brings health data from different organizations into one clean, consistent shape. Data cleanliness was the whole point: get the foundations right, and ML and AI can genuinely learn across health data organizations instead of choking on their differences.
It’s the same conviction behind our work on Kamino at Yale: in healthcare AI, the outcome is determined upstream. Models are only as good as the data discipline underneath them.
Sitting on data your AI can’t learn from? That’s a solvable problem. Get in touch.