An AI utilization-management assistant has reviewed a single Medicare Advantage prior-authorization request and proposes a high-blast-radius 3-action bundle — deny prior auth + send denial notice + close request. This page is a guided demo: pick a persona, review the request, submit the AI's proposal to HELM, see the deterministic decision, then take the next-step action that belongs to your role.
Four steps. Each persona sees the same prior-auth request differently and takes different next-step actions. Try at least two personas to feel the architecture.
Five roles in the same health plan. Each has different authority, different views, and different next steps.
Same prior-auth request for everyone — but the framing card shows how your role reads the situation.
The deterministic gate evaluates 6 checks. The eval is the same regardless of persona — HELM is not opinion-based.
After the decision, your persona panel on the right shows what you can actually do next. The interactive ones expand inline.
A health plan can't answer a CMS auditor — or a patient's appeal — with "the model decided." When AI touches a coverage decision, the burden of proof is yours, and the law now says a licensed clinician, not an algorithm, must own every medical-necessity denial. HELM is the layer that lets you carry it. Not a policy you promise to follow; a control that is structurally true.
Under CA SB 1120 and CMS-4201-F an algorithm may not be the basis — wholly or partially — for a medical-necessity denial. HELM routes the determination to a qualified clinician and records who decided. always escalate.
A determination notice without specific clinical rationale and full appeal rights — the full-and-fair-review standard — cannot send. The gate is the control, not a checklist someone forgot.
No 1.2-second batch denials. Each decision shows the member's individual clinical circumstances were considered and the review was substantive — the exact anti-pattern the nH Predict and PxDx suits were built on.
The same signed evidence chain answers CMS, your state DOI or DMHC, OCR, and a plaintiff in discovery. Medical necessity, fairness (HTI-1 / §1557), and appeal rights all read from one source of truth.
KAiM's founder spent 25 years governing high-stakes decisions inside the most examined institutions in America — the Federal Reserve Bank of San Francisco and Wells Fargo — building the model-risk, evidence, and continuous-monitoring discipline that survives a regulator's review. The same examination standard now applies to AI in care: HELM puts governance into the decision, not on top of it — the control we wished existed before an algorithm ever denied a claim.
The way KAiM engages is concrete: pick one high-risk AI-assisted decision you're worried about — prior authorization, claim denials, utilization management, appeals, level-of-care, step therapy. We map it to actor / action / authority / policy / evidence / escalation, then show how HELM gates it. No deck. No demo theater. A working session with your medical-director and compliance teams.