Building Healthcare AI That Doesn't Break at 3 AM

Let's be honest: most healthcare ML demos look great in a Jupyter notebook. Then they hit production—and stumble.
Why? Because notebooks don't have time pressure, incomplete documentation, or clinicians who need to trust the output in seconds.
After 12 years in pharmacy, I approach healthcare ML differently. Here's my checklist for production-ready clinical models:
✅ Feature discipline: Only use data available at decision time. If it arrives after triage, it doesn't belong in your model.
✅ Interpretability by default: SHAP values aren't enough. Can a nurse understand why the model flagged this patient? If not, iterate.
✅ Fairness as architecture: Don't remove demographic features to "fix" bias. Adjust decision thresholds per subgroup—and document why.
✅ Utility over accuracy: AUC-ROC won't tell you if your model helps. Decision curve analysis will.
✅ Deployment realism: Sub-second inference isn't a nice-to-have. It's the price of admission for triage support.
I've seen too many promising models gather dust because they weren't built for the messiness of real care. Let's change that.
I am open to remote roles globally.
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HealthcareAI #MachineLearning #ProductionML