Emergency departments, theatres and wards run hot and variable. Stochastix builds patient-flow digital twins and capacity models so you can plan beds, staff and pathways with evidence, and prove a change will work before it touches a patient.
Arrivals surge by hour, day, season and event. Rosters and capacity built on averages can't absorb the peaks that actually cause harm.
A bottleneck in one ward becomes access block in ED and cancelled surgery upstream. The system fails as a whole, not in isolation.
Maximising elective throughput while protecting emergency capacity and shrinking waitlists is a constant scheduling trade-off.
New wards, models of care and staffing changes are expensive and hard to reverse. Decision-makers need evidence they'll work first.
Simulation, optimisation and forecasting, delivered as live dashboards and planning tools your operational and executive teams use.
Discrete-event models of ED, wards and whole-of-hospital flow to test fast-track, staffing, bed numbers and models of care, safely, in silico.
Optimised theatre lists and workforce rosters that lift utilisation and match staffing to forecast demand without burning out teams.
Hour-by-hour, specialty-level forecasting of presentations and admissions so capacity and staffing plans are built on signal, not gut feel.
Worked examples of how we approach healthcare problems, and the decision each one informs.
A patient-flow digital twin tests changes like a fast-track stream or revised senior-doctor coverage against a year of real arrival patterns, so you can see the effect on access block before changing anything on the floor.
Optimised elective theatre allocation across specialties and sessions balances case mix against emergency reserve, delivered as a tool surgeons and schedulers run each planning cycle.
Forecasting and simulation of alternative ward configurations under projected demographic demand give the board a defensible long-range capital plan, not a single-point guess.
Tell us about the flow problem or the business case in front of you. We'll tell you honestly whether a model will help, and how we'd build it.
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