Quick calculators and advanced simulators for hospital management to make data-driven business decisions.
Sector context
The CFO of an SMB private clinic faces a counter-intuitive equation: bed occupancy isn't revenue, pharmacy inventory expires unannounced, and a shift in payer mix (private, insurance, public) can change gross margin without a single additional patient walking in. Every empty bed still costs nursing, cleaning, energy, and depreciation. Every expired drug becomes direct margin loss. The healthcare simulators help model capacity, clinical inventory turnover, and cost per service — without replacing clinical staff judgment.
Key metrics
Indicators an SMB operator in the sector should know before modeling decisions.
Bed occupancy rate
Occupied bed-days / available bed-days. A clinic sustaining 75–85% is healthy; above 90% usually signals bottlenecks in discharges or admissions.
Average length of stay (ALOS)
Days the average patient stays per episode. It drops when discharge processes speed up; rises when imaging or lab shifts are missing.
Cost per discharge
Total operating cost / discharges in the period. Useful for benchmarking against insurer tariffs and spotting patient types that lose money.
Pharmacy inventory turnover
Drug COGS / average inventory. A typical clinic should turn 8–12 times a year; less means imminent expiry.
Margin by service line
Imaging, lab, inpatient, and outpatient have different cost structures. Knowing margin per line drives capacity and scheduling decisions.
How to pick the right simulator
If your question is about clinical capacity (do I need one more bed this year? how would 95% occupancy feel?), start with the bed management simulator. If the bleeding is in the internal pharmacy, the drug turnover simulator shows how much inventory you have frozen and which SKUs risk expiry. If you need to defend tariffs with an insurer, the clinical cost simulator breaks down the cost per service. For medical staff planning against seasonal demand, the medical-consultation demand simulator sizes shifts.
Practical example
Hypothetical case in US dollars. Plug your real numbers into the simulator to validate your own scenario.
A 30-bed private clinic runs at 72% occupancy and an ALOS of 4.2 days. Its monthly operating cost is $185,000 USD. The team models a discharge-process improvement that cuts ALOS to 3.8 days without changing admissions. The simulator shows bed turnover rising 10.5%, equivalent to 2.3 extra discharges per day; with an average discharge margin of $420 USD, that's $29,000 USD extra monthly revenue without hiring nurses or expanding the facility. The $12,000 USD spent on training the discharge team pays back in less than 14 days.
Common modeling mistakes
Traps we see when reviewing sector planning. Avoid them before closing your own model.
Treating occupancy as linear revenue
A bed occupied by a complex patient costs more than two outpatients combined; aggregating bed-days without segmenting by complexity inflates apparent margin.
Modeling the pharmacy as a single inventory
Antibiotics, oncology drugs, and chronic-care meds turn over at very different speeds. A single aggregate turnover hides the SKUs that are expiring.
Forgetting payer mix
Moving from 60% private / 40% insurer to 30% / 70% can sink margin without changing volume. Model payer mix as a variable, not a fixed input.
Assuming fixed clinical staffing
Nursing costs are stepped by shift and by patient-nurse ratio. A 10% occupancy bump isn't 10% more nursing — unless it forces opening a new shift.
Scope and limitations
Healthcare simulators are financial and operational tools, not clinical ones: they don't replace medical practice guidelines, patient safety protocols, or local health regulation (COFEPRIS, INVIMA, ANMAT, FDA, or equivalent). Decisions touching quality of care require sign-off from the institution's medical committee before implementation.
Start here
The simulators with the most adoption in the sector. Each tackles a different business question.
Bed management
Models occupancy, ALOS, and turnover to defend capacity without over-building.
Clinical costs
Breaks down cost per service (imaging, lab, inpatient, outpatient).
Drug turnover
Spots pharmacy SKUs at risk of expiry before manual control does.
Consultation demand
Sizes shifts and clinical staff against real seasonality.