Demonstration prototype by Remington Data Systems LLC. Synthetic data only. Not affiliated with or endorsed by CDC. Do not enter real health information.

Technical approach & risk register

This page makes the engineering behind the prototype explicit: how what you can click today maps to the proposed production architecture in the CDC-managed Azure environment, the measured performance evidence, the Section 508 approach, and the top technical risks with their mitigations.

Architecture: prototype today → production in CDC-managed Azure

The prototype is deliberately built the way the production front end would be built: a static, configuration-driven rendering layer with no framework runtime and no external requests. In production, that same front end is delivered from CDC-managed Azure, and the intake, workflow, storage, and transmission concerns move to managed platform services behind it.

Performance evidence

The margin is structural, not incidental. A static-first front end has no server render time and no framework boot cost, so first load is dominated purely by transfer size — and ~150 KB arrives in well under a second on a typical mobile connection. Because rendering is configuration-driven, the payload also stays flat at scale: adding questions, branching rules, or entire form versions grows the configuration by kilobytes, not the application by megabytes, and content changes ship without redeploying code. Static assets are trivially cacheable at the Front Door edge, so surge events (a new vaccine campaign, a media-driven reporting spike) scale on CDN economics rather than server capacity.

Accessibility (Section 508) approach

Practices already implemented and inspectable in this prototype:

Production plan: accessibility is verified continuously, not at the end — automated checks in CI on every change, assistive-technology testing (JAWS, NVDA, VoiceOver, and mobile screen readers) each iteration, and a formal Accessibility Conformance Report (ACR/VPAT) with a remediation plan delivered with the Beta release, as the PWS requires.

Risk register

Top six technical risks, with likelihood, impact, and the mitigation built into the approach.

Likelihood and impact rated Low / Medium / High.
Risk Likelihood Impact Mitigation
Integration with existing VAERS ingestion contracts Medium High Early interface-control-document (ICD) validation at kickoff; contract-first development against stubs — exactly as this demo's FHIR-shaped output already stubs the transmission payload — so integration surprises surface in week one, not at delivery.
ATO / security-authorization timeline Medium High Compliance-as-code from day one: Security and Privacy package drafting starts at kickoff, evidence is mapped to controls as it is produced, and the architecture leans on CDC-managed Azure platform services with established inheritance rather than bespoke infrastructure.
Plain-language comprehension gaps driving incomplete reports Medium Medium Iterative usability testing with consumer participants each build cycle, and a branching design that asks only relevant questions — plus in-form escape hatches ("describe it in your own words") so a reporter is never blocked by vocabulary.
PHI/PII handling in file uploads Medium High Two-phase upload: files land in a quarantined staging container, are scanned, and only then promoted. Encryption in transit and at rest via platform services; least-privilege access through managed identities; no PHI in logs.
AI suggestion-tool accuracy and bias Medium Medium Human-in-the-loop by design — suggestions assist, never auto-decide; every suggestion is provenance-linked to its source text; accuracy and bias are monitored and reported under the AI Compliance & Risk Management Plan.
Schedule compression on a 9-month period of performance High Medium The config-driven architecture removes the slowest class of change from the critical path — content, wording, and branching evolve without deployments — and the iterative build cadence the PWS prescribes delivers working software every cycle, so schedule risk is visible early instead of discovered late.

Scalability and extensibility

One schema already serves two audiences: every question carries plain-language and clinical wording plus per-mode visibility, and the same engine renders the public wizard, the provider wizard, the admin preview, and the review screen. That is the extensibility mechanism, demonstrated rather than promised: