How do I use the Monte Carlo Simulator? To use the Simulator, treat it as a structured “uncertainty lab” that uses the same Kypholift risk and ROI logic as the point-estimate dashboards. Begin by selecting a scenario (Conservative, Moderate, Aggressive) so the simulator loads defensible starting priors for supine intolerance and other uncertainty parameters. Next, review the eligibility inputs p(age ≥ 60 | scan), p(hyperkyphosis | age ≥ 60), and p(supine intolerance | hyperkyphosis), as these three terms determine the size of the eligible cohort and often dominate the value. Then enter your annual exam volumes by modality (MRI, CT, PET) and, if the simulator exposes it, confirm the device program inputs (rooms adopting, cost per room, annual operating cost). After volumes, examine the distributions of event probabilities and costs for each loss channel (repeats, aborts, falls, pressure injury, sedation escalation, diagnostic claim linkage). The key workflow move is to keep your first run conservative, run a quick simulation to ensure outputs behave logically when you change one assumption, then run a full simulation (for example, 20,000 iterations) once you are satisfied that your inputs match the story you want to defend in front of finance, risk, and clinical leadership. Use the Export JSON feature to save the exact scenario configuration you presented, because that file functions as your decision log and makes it easy to reproduce results later when the committee inevitably asks, “What assumptions did we use?”

What does the Monte Carlo Simulator tell me? The simulator tells you not only what the “most likely” savings could be, but also how wide the plausible range is and which assumptions actually control that range. Instead of a single annual savings figure, you will see a distribution with a median and uncertainty bounds (often 5th to 95th percentiles), which is the most useful way to communicate risk and value for patient safety interventions where event rates and legal severity have long tails. This matters because the economics here are not driven solely by predictable operational churn, such as repeats and aborted slots. They are also influenced by low-frequency, high-severity outcomes such as serious falls, pressure injuries, and severe sedation events, and these outcomes exhibit fat-tailed behavior that a simple average can disguise. The Monte Carlo output shows whether your business case remains positive even when parameters land on pessimistic combinations, and it identifies the dominant drivers through sensitivity analysis (for example, Spearman rank correlations). If supine intolerance probability and fall-related legal cost dominate sensitivity, the simulator is telling you where to focus real-world measurement and governance: tighten your workflow capture for intolerance and aborted studies, improve event reporting for near-falls and transfers, and partner with risk management to use internally credible cost anchors. In other words, the simulator does not just produce a number. It tells you how confident you should be in that number, what could break it, and what data you need to collect to convert a plausible model into a defensible, scalable adoption plan.

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