Machine learning models that support care teams—not replace judgment
For a healthcare client focused on population health, we delivered classical ML models (gradient-boosted trees and regularized linear baselines) alongside monitoring—because not every problem needs a frontier LLM. The goal was decision support: surfacing likelihoods and drivers, not automated diagnoses.
Data and fairness
We worked with their data governance council on feature sets, label definitions, and cohort exclusions. Leakage checks and temporal splits were non-negotiable—models that “peek” at the future erode trust instantly in clinical settings.
Explainability
Care teams asked “why this score?” We exposed global feature importance and local explanations where appropriate, paired with clear UI copy that the output was advisory and subject to protocol.
MLOps that survived audits
- Versioned training pipelines and model cards
- Drift detection on inputs and outcomes
- Rollback paths when performance degraded after EHR upgrades
This post discusses engineering practices only. Models were reviewed in client governance processes; details are anonymized.