Many healthcare ML projects never reach production.
The Problem
The model works—but the system is not ready.
Common Issues
No integration with EHR or clinical systems
Undefined decision pathways
Lack of monitoring and feedback loops
No ownership for deployment
What Deployment Requires
API or system integration
workflow alignment
clear user interaction points
continuous performance monitoring
Practical Shift
Build with deployment in mind:
design for real users
define actions tied to predictions
plan for system constraints
Key Insight
In healthcare, a model is not complete until it is deployed and used.
I am open to remote roles globally.
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