Accurate capacity planning is critical for emergency departments, yet access to realistic patient arrival data is often limited by privacy and data availability constraints. This session demonstrates how SAS can generate fully synthetic datasets that simulate future emergency‑department arrivals. Using patterns from administrative health data common across Canadian health systems, attendees will see how SAS models daily and hourly arrivals, assigns acuity, demographics, and diagnostic needs, and produces reproducible, stochastic data representing future demand. Learn how synthetic patient data supports operational modeling, patient‑flow analysis, and bed‑utilization planning for emergency care systems.
A constant stream of patients, rising demand, and limited capacity—how do emergency departments keep up? In this session, Owen Brown shows how synthetic data generated with SAS can recreate realistic patient arrival patterns, acuity levels, and care needs without exposing sensitive data. Watch how these simulated inputs power advanced operational models to test staffing scenarios, reduce wait times, and improve patient flow before real-world changes are made. If you’re curious how data science can reshape healthcare planning, this is a compelling look at turning simulated insight into real operational impact.
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