Generative Simulation Benchmarking for precision oncology clinical workflows during mission-critical recovery windows

Description and use

Generative simulation enables assessment of workflow recovery in precision oncology, considering not just model accuracy but throughput and fairness during post‑outage catch‑up.

Problem

Clinical decision‑support models for precision oncology can be unavailable for hours, but existing benchmarks focus only on model accuracy, ignoring how the surrounding workflow recovers during the critical catch‑up period.

Outcome

The simulation revealed that naive schedulers prioritize low‑urgency, low‑complexity cases, causing critical cases to backlog during recovery. The benchmark framework allows teams to evaluate and improve workflow resilience before real outages occur.

Workflow

• Generate realistic oncology cases with structured variant priors and urgency levels
• Inject faults such as outages, latency spikes, partial data loss, and model rollbacks
• Run the full pipeline and measure recovery metrics: time to steady state and fairness across urgency tiers

Evidence

We were studying precision oncology pipelines — the kind that ingest genomic variants, match them to targeted therapies, and surface recommendations to tumor boards.

Source

Instead of waiting for real outages to test workflows, we can generate plausible clinical scenarios, inject controlled failures, and measure how the workflow recovers.

Source