WPS203IntermediateBreakout sessionPublic Sector Playbook 5 live updates

Optimising Outpatient Waitlists with ML at Gold Coast Health

What this session is about

Deploying ML in high-stakes environments demands enterprise readiness, governance, and continuous monitoring. In this session, you'll learn how Gold Coast Health moved from pilot to production with a predictive model identifying patients unlikely to attend procedures — achieving 33% precision, doubling the 15% manual baseline — while ensuring fairness across cohorts. The session covers real-world ML architecture on Amazon SageMaker Pipelines, production monitoring including data quality, pipeline health, and drift detection, plus navigating AI governance through bias analysis and impact assessment. Whether you're in healthcare, financial services, or any regulated industry, walk away with actionable patterns for deploying responsible ML at scale.

Playbook

Editorial commentary · what to actually do about this on Monday

The concept
ML predicting outpatient no-shows. 33% precision, doubling the 15% manual baseline. Production-grade with fairness analysis and drift detection.
Why it matters
No-shows are expensive (idle clinical capacity); even modest improvement compounds across thousands of slots.
The hard parts
Bias is a real concern. "Likely to no-show" must not correlate with protected attributes. Fairness analysis is non-optional in healthcare.
Playbook moves
(1) Bias analysis at every model release. (2) Document the impact assessment. (3) Make the model interpretable to clinicians who'll act on it.
The surprise
33% precision means 67% false positives. Deployment success depends on what you *do* with the prediction — calling patients vs. removing slots vs. double-booking. The model is only as good as the workflow around it. Build the intervention design before chasing higher precision. ---

Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.

Live updates related to this session LIVE

Sourced via Parallel AI Monitor — continuous web watch on 21 topical streams. Updated .

External links matched to this session via topic relevance. The KB does not endorse third-party content; verify before citing.