Context
Public and private surgical systems operate under constant demand pressure, where limited resources force difficult prioritization decisions. These decisions often involve clinical urgency, waiting times, and organizational constraints.
What we do
This project develops data-driven decision-support tools that assist surgical scheduling under real operational conditions. By combining predictive modeling, optimization, and clinician expertise, the work supports adaptive and transparent prioritization processes. The collaboration reflects long-term institutional integration with Chilean public healthcare services.
Why it matters
The project helps hospitals move from reactive scheduling toward structured, evidence-informed decision-making.
