Designing Decision-Aware Clinical Machine Learning Systems

Most ML tutorials end at model evaluation.

Healthcare systems begin at decision-making.

A predictive model for hospital readmission may show strong discrimination and calibration. But without a defined intervention protocol, it does not change outcomes.

To design decision-aware clinical ML systems, consider:

1. Action Mapping

Define:

Who receives predictions

At what time

What action is triggered

What resource constraints apply

1. Threshold Optimization

Choose thresholds based on:

Intervention capacity

Cost of false positives

Clinical risk tolerance

1. Cost-Sensitive Evaluation

Optimize not only statistical performance, but operational utility.

1. Workflow Simulation

Assess integration impact before deployment.

1. Post-Deployment Monitoring

Measure downstream outcome changes — not just predictive accuracy.

Healthcare ML must evolve beyond leaderboard thinking.

My focus lies at this intersection:

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

You can follow my broader discussions here:

Medium: https://medium.com/@fora12.12am

Substack: https://substack.com/@glazizzo

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LinkedIn: www.linkedin.com/in/onyedikachi-ikenna-onwurah-0a8523162

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