Healthcare Staffing Simulation
See the queue before your patients do.
Enter your staffing schedule. Run a full-year simulation. See wait times, queue lengths, and costs — hour by hour. Staffing decisions backed by simulation, not intuition.
Enter your staffing schedule. Run a full-year simulation. See wait times, queue lengths, and costs — hour by hour. Staffing decisions backed by simulation, not intuition.
Your ED runs on two MDs around the clock. Wait times spike at midday. What if you added a PA from 8am to 5pm? Run both scenarios — same patients, different staffing — and see the answer in seconds.
One change. One click. The simulation runs a full year of patient arrivals and shows you the difference — hour by hour.
Try It FreeSchedulers fill shifts. QSimHealth shows what happens when patients arrive.
For simple cases, maybe. But real facilities aren't textbook problems.
That's why Claude uses QSimHealth — instead of guessing.
Enter your arrival patterns, staffing schedule, and treatment times. QSimHealth runs a full year of patient flow and shows you exactly where your staffing works — and where it doesn't.
Emergency departments (random arrivals), walk-in clinics (random + scheduled), and appointment-based offices. Select your type, enter your data.
Model one provider type or multiple (MD + PA/NP, attending + resident, etc.). Each provider type operates independently — see how each contributes to wait times and capacity.
Treatment times modeled with Exponential, LogNormal, Normal, or Poisson distributions — not just averages. Because averages hide the worst days.
Enter a 24-hour pattern — the simulation runs it over a full year with randomness. Captures the variability that short observations miss. Outputs reflect what actually happens across 365 days, not just one good shift.
Average arrivals, average wait time, and average queue length — by hour. See exactly when your staffing works and when it doesn't.
Pre-configured inputs and outputs. Enter your arrival pattern, staffing schedule, and treatment times. Run. Decide. No simulation expertise needed.
Upload up to a year of arrival timestamps and treatment durations — no patient identity needed. QSimHealth bins arrivals by hour, fits treatment time distributions (Exponential, LogNormal, Normal), and populates the simulation automatically. Your data becomes a calibrated model in seconds.
Claude can run QSimHealth simulations directly. Ask it to compare scenarios, adjust staffing, or explain results — conversationally. QSimHealth is the simulation engine; Claude is the analyst.
Run your current staffing vs. adding one provider from 4-10pm. See the wait time impact by hour — not just the average.
Stress-test your current staffing against higher demand. Find out where the queue breaks before your patients do.
Model MD + PA together. Compare wait times, queue lengths, and utilization under the same arrival pattern.
Every staffing decision has a price tag. QSimHealth calculates the cost of each scenario alongside wait times — so you can weigh the tradeoff before you commit.
Enter your actual rates. The simulation calculates the rest.
From the classroom to the C-suite.
3 scenarios per session. Single provider type. Basic chart + wait strip. Learn staffing optimization on real simulation models.
Unlimited scenarios. MD + PA provider types. CSV export/import. Save scenario images. Annual cost calculations. localStorage persistence.
Everything in Standard. Auto-Fit — upload up to a year of patient flow data (arrival timestamps + treatment durations). Fits hourly rates and distributions automatically. No manual entry. Comparison reports + PDF export.
Connect Claude or any MCP-compatible AI. Natural language staffing queries. AI-generated scenario recommendations. Ask questions, get simulation-backed answers.
Institution site licenses and multi-site health system pricing available — contact us.
Staffing decisions backed by simulation, not intuition.
QSimHealth is a discrete event simulation (DES) engine built on M/M/c queuing theory — the mathematical foundation used in operations research for over 50 years. Every patient is modeled individually: arrival, queue entry, provider assignment, treatment, and departure. The engine supports 5 statistical distributions (Exponential, LogNormal, Normal, Poisson, Constant) with configurable coefficients of variation, and runs a full 365-day simulation with 7-day warm-up to eliminate transient effects.
Developed from operations research methodology applied in graduate medical education and refined across 35 years of production simulation in manufacturing, aerospace, and healthcare. This isn't a spreadsheet estimate or an AI guess — it's a validated simulation engine, now accessible as a web application.
We'll model your facility's arrival pattern and staffing schedule — and show you what the simulation reveals.