Evaluating Clinical ML as an Intervention, Not Just a Model

#ai #datascience #machinelearning #monitoring

Clinical ML evaluation typically emphasizes:

• Discrimination
• Calibration
• Cross-validation performance

These are foundational.

But insufficient.

When deployed, a model:

• Changes clinician workflow
• Alters escalation patterns
• Consumes limited resources
• Introduces false positive burden

This makes it an intervention.

Intervention-level evaluation requires:

Prospective impact studies

Cost-effectiveness modeling

Equity audits

Workflow load assessment

Continuous monitoring pipelines

For example, a sepsis prediction model may:

• Improve early antibiotic delivery
• Increase unnecessary ICU admissions
• Trigger over-testing

Only system-level measurement reveals net benefit.

Healthcare ML must evolve toward intervention science.

My background:

Pharmacist (12 years)
MPH
MSc Data Science – Precision Medicine

Focused on decision-aware and impact-measured clinical AI systems.

You can explore my broader work here:

Open to remote roles and collaborations in healthcare AI and digital health systems.

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