Controls as code
Guards live in the workflow engine, not in policy prose. Illegal transitions are HTTP 409s, not training slides.
Every control described on this page is enforced in code — in the state machine, the permission layer, and the audit spine — and can be independently inspected against the running system. Nothing here is a policy PDF promise.
Apex MediSuite is a prior-authorization and value-based-care orchestration platform for a domain where a wrong decision is a legal event. So the platform treats security and decision governance as the same discipline: statutory constraints are compiled into the workflow state machine, every privileged action requires an authenticated, credentialed actor, and every state change lands in an append-only, hash-chained audit trail.
The hosted environment you can sign into today is a live demo environment running entirely on synthetic data — no PHI is stored or processed. The controls, however, are built as if the data were real: the same guards, the same audit chain, the same server-side refusals. You can verify that yourself — the audit integrity endpoint (/ops/audit-verify) recomputes the hash chain on demand, and the Auditor demo role gives read-only access to full decision traces (credentials in the manual).
Guards live in the workflow engine, not in policy prose. Illegal transitions are HTTP 409s, not training slides.
Hash-chained audit trail with a public self-check endpoint; correlation IDs tie every UI action to server events.
No AI or automation path can issue an adverse determination. The deny transition demands a physician credential.
Every patient, payer, and policy in the demo is fictional. A deployment flag pins the environment to synthetic-only mode.
Apex MediSuite is designed to align with the frameworks below. We state our position precisely: where a control is enforced in code today, we say so; where alignment is a design target or a roadmap item, we say that instead. We do not claim certifications or attestations we do not hold.
| Framework | What it demands | Where Apex MediSuite stands | Status |
|---|---|---|---|
| HIPAA Security Rule incl. Jan 2025 NPRM direction (final rule expected ~2027) |
The proposed rewrite removes the required-vs-addressable distinction and points to mandatory MFA, encryption in transit and at rest, asset inventory and network mapping, prescriptive risk analysis, and vulnerability-scanning cadence. | Built toward the NPRM, not the legacy floor: MFA slot (TOTP) on every account with an enforcement flag; TLS-only transport with HSTS; Argon2id password storage; documented deployment checklist covering asset inventory and least-privilege database users. Field-level encryption at rest with managed keys is a tracked roadmap item. | Designed to align |
| CMS-0057-F Interoperability & Prior Authorization Final Rule |
Since Jan 1, 2026: 72-hour expedited / 7-calendar-day standard decision timeframes, specific denial reasons (no generic denials), and annual public PA metrics. From Jan 1, 2027: Prior Authorization, Patient Access, Provider Access, and Payer-to-Payer APIs. | SLA clocks are computed server-side at submission (72h/7d, configurable per payer type — scope is never hard-coded). The deny transition returns HTTP 422 without a reason code plus reason text. PA records carry channel provenance for metrics capture. API alignment path follows the Da Vinci CRD / DTR / PAS pattern (FHIR facade on the roadmap). | Enforced in code |
| CA SB 1120 "Physicians Make Decisions Act" + NAIC AI Model Bulletin (~25 states adopted) |
AI may not autonomously deny, delay, or modify care on medical-necessity grounds; a licensed physician with relevant clinical expertise must make the determination; plans must disclose and audit AI use. The NAIC bulletin adds a written AI-systems program with model validation and vendor oversight. | Enforced server-side in the state machine: the deny transition requires the pa.decide permission and a physician credential (MD:*) — an RN attempting a denial is refused by the server regardless of what any client sends. AI output is written as advisory rows that structurally cannot touch case state, and every advisory is logged with model and version for audit. | Enforced in code |
| 42 CFR Part 2 Substance-use-disorder records |
Heightened confidentiality and consent handling for SUD treatment records, requiring segmentation from the general record. | Segmentation hooks are a tracked design item on the production path; the current demo carries no SUD data (all records synthetic). We state this plainly rather than implying coverage we have not built. | Planned |
| SOC 2 (CC-series) Trust Services Criteria |
Independent attestation over security, availability, and related common criteria — the entry ticket for US enterprise procurement. | SOC 2-aligned controls: access control with least privilege, change-tracked configuration, structured logging, backup and restore testing, incident-handling runbooks on the deployment checklist. Formal Type II attestation is on the roadmap and would accompany any production engagement; we do not claim it today. | Aligned controls |
Wording note: "designed to align" means the control objectives shaped the architecture and the relevant mechanics exist in code; it does not assert regulatory compliance status or a third-party attestation, neither of which apply to a demo environment holding no PHI.
Six domains, every item below verifiable against the running system or the deployment configuration. Items we have deliberately deferred are listed later in shared responsibility & roadmap — not silently omitted here.
Recent litigation over algorithmic utilization management has made one thing clear: when a determination is challenged, the algorithm, its inputs, and its audit trail become discoverable. Apex MediSuite is built for that day. Every prior-authorization decision stores the policy package version it was evaluated against, the five-state outcome of each criterion, any AI advisory with its model version, the reviewer's identity and credential, and timestamps for every transition — all bound into the hash-chained audit trail. A historical decision can be replayed exactly as it happened. "The algorithm did it" is a sentence this system cannot produce, and "here is precisely what happened" is one it always can.
AI in utilization management is now constrained by state statute — California's SB 1120 requires that a licensed physician with relevant clinical expertise, not an algorithm, makes medical-necessity determinations, and the NAIC model bulletin (adopted in roughly 25 states) requires a governed, auditable AI program. Apex MediSuite's answer is structural, not procedural: the rules below are properties of the system's architecture.
Advisories are generated only from the tenant's own policy packages and the case record in front of the reviewer — never from open-ended generation.
Every recommendation cites the criterion and the evidence item it rests on, rendered visually distinct from deterministic rule results.
Each advisory row records model and version in the audit trail — the registry needed for the AI-use audits state regulators now expect.
Advisory rows have no write path to case state. A compromised prompt cannot deny care, because no code path connects AI output to the deny transition.
Try it live: sign in as the UM Nurse demo user, attempt a denial, and the server will refuse it — then repeat as the Medical Director with a specific reason. The guard is in the API, not the UI.
The demo environment operates under a strict synthetic-data policy: every patient, member ID, coverage, and clinical value is fictional and generated for demonstration. There is no PHI in the system — and the handling rules below are enforced as if there were.
A trust page that lists only finished work is a marketing page. This section is the other half: the controls a production, PHI-bearing deployment requires that the demo environment deliberately defers — each tracked, each part of the production-customer path.
If you believe you have found a vulnerability in Apex MediSuite or this demo environment, we want to hear about it. Report privately — include the URL, steps to reproduce, and the correlation ID from the response header if you have one. We will acknowledge receipt, investigate, and keep you informed through remediation.
security@bvlogic.example (placeholder address for the demo environment)
Please avoid testing that degrades the service for other demo users, and remember the environment holds synthetic data only — there is no PHI to find. We do not operate a paid bug-bounty program at this time and make no reward commitments; we do commit to taking every credible report seriously and crediting researchers who wish to be named.