Designed by Kelly Emrick, DHSc, PhD, MBA

How To Use The Simulator: The above Medicaid Work Requirements Impact Simulator guides you through a straightforward yet impactful process on a single screen: first, you define the hospital’s profile; then you apply a coverage shock; and finally, you interpret the impact on operating margin in both dollars and percentage points. You begin by selecting the hospital type (rural acute care, urban safety net, urban non-safety net, or custom), entering annual net patient revenue in millions, the current operating margin, and the share of revenue from Medicaid. Optionally, you can refine the payer mix across Medicaid, Medicare, commercial, and self-pay, with a live “sum check” to ensure the inputs remain realistic. Next, you use the sliders to model policy changes: one slider determines how many Medicaid patients lose coverage (0-30%), and another estimates how much of that lost Medicaid revenue will be recovered through exchanges, self-pay, philanthropy, or internal cross-subsidy. Pre-set buttons provide quick scenarios like “no work requirements,” “moderate loss,” or “severe loss,” and two toggles allow you to activate federal and state mitigation, each with its own percent offset, to see how much relief would be needed to sustain your position. On the right side of the simulator, the tool translates these decisions into a financial overview. It displays your baseline margin and operating income, your scenario margin and income, and the remaining “revenue at risk” after all mitigation. A color gauge visualizes the change by moving a pointer from green toward yellow or red as the margin shrinks. Simultaneously, a health label categorizes the hospital as stable, vulnerable, distressed, or negative based on both the margin level and hospital type. A narrative sentence then clearly explains what happened in plain language, restating the coverage loss, the net revenue loss in millions, the change in margin, and the new margin for that hospital type. When you test different combinations, the data quickly reveal three key insights: thin margins can worsen rapidly when a modest share of Medicaid patients lose coverage, rural and safety net hospitals become distressed faster than large urban systems under the same policy shock, and even optimistic assumptions about exchange participation and public relief usually soften the impact rather than eliminate it. Used this way, the simulator shifts from being just a forecasting tool to a strategic conversation starter, helping boards and executives understand how coverage policy, payer mix, and mitigation options interact, and where their organization stands on the spectrum from resilient to vulnerable.