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SANO LABS
Healthcare Economics & Modeling Tools
Dive into an expanding library of dynamic calculators and modeling tools designed with real healthcare,, and actuarial frameworks! Whether you're tackling Medicare Advantage MLR impact analysis or diving into value-based care financial modeling, these tools empower health plan executives, ACO operators, and strategy teams to crunch the numbers quickly and efficiently—no dedicated analyst required!
The MA New Cohort MLR Impact Modeler
Quantify Actuarial Risk & Forecast Margin Drift with Geographic Precision
BRIEF UTILITY OVERVIEW - Goal & stategic use case - what is does & why/when to use, embedded dynamic data architecture, statistical/ultivariate analysis framework, geo-adjusted/county level dynamic data sourcing (e.g. historical county level member mix + associated starting RAF, CMS rate data + V28 sensitivity modeling, Stars QBP impacts, plan product type specific customization, UX overview, etc.
1. Goal & Strategic Use Case:
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What it does: This tool quantifies the immediate medical loss ratio (MLR) margin dilution caused by adding new Medicare Advantage members, solving the structural "timing problem" between immediate medical costs and lagged risk-adjusted revenue.
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When & Why to use it: Use it during product strategy, network development, or M&A evaluation to accurately forecast how a new cohort will impact your total book of business's bottom line before their coding matures.
2. How It Works: Embedded Data Architecture:
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Geo-Deterministic precision: It projects revenue and cost by embedding five public data layers—including the complete CMS 2026 Rate Tables for 3,248 counties across 56 jurisdictions—directly into the calculation engine.
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Zero external dependencies: Requiring no external APIs, the tool securely computes weighted-average benchmarks matched to your exact service footprint and quality bonus Star tiers.
3. Statistical Framework & Mathematics:
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Multivariate computation: Operating as a deterministic scenario engine within a 12-dimensional parameter manifold, it uses nonlinear cost functions and bilinear revenue functions to calculate MLR Drift (the exact basis-point shift in your overall MLR).
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Actionable coding targets: The model computes exact break-even Risk Adjustment Factors (RAF) using $\theta* BE(s) = K(s) / \bar{B}$, providing your clinical coding teams with concrete, segment-level targets to achieve margin neutrality.
4. Segment Deep-Dive Variables:
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Actuarially distinct profiling: Incoming members are stratified into three distinct segments: Age-Ins (lowest initial demo RAF of 0.40, zero history), Switchers (partial RAF carry-over with a 5% "pent-up demand" cost surge), and D-SNP (high acuity, high absolute cost, but potentially margin-accretive).
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Granular assumptions: Each segment features independent, user-configurable variables for demographic vs. true RAF, historical cost PMPM, medical trend, and utilization adjustments.
5. Geo-Adjusted Member Mix:
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Evidence-based composition: Instead of relying on guesswork, the tool’s "Standard Member Mix" automatically derives your expected enrollment channels at the county level using independent public data signals.
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Triangulated modeling: It applies a market step-function to CMS MA penetration rates (dictating the Age-In vs. Switcher split) and scales KFF state-level D-SNP enrollment shares to output a highly localized, auto-normalized member mix.
6. Interactive Toggles & Outputs:
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Flexible scenario modeling: Use the Standard Member Mix toggle to instantly generate geographically calibrated baselines, or switch it off to unlock the 9 segment sliders for manual sensitivity testing.
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Executive-ready KPIs: The dashboard translates these inputs into 9 primary output metrics, instantly revealing your blended cohort MLR, the absolute dollar impact on margin, and the critical revenue coding gap you need to close in Year 1.