A defensible Home Health Hub architecture
engineered as a hardware + data ecosystem

The hardware layer centers on the AegisOne Home Health Hub: a stationary, countertop device with a 10-inch touchscreen and input mechanisms for strips and cartridges.

• Strip/cartridge formats for tests such as COVID, flu, strep, UTI, pregnancy, ovulation, glucose, lipids, thyroid & hormones
• Standardized loading & guidance workflows for each test type
• Device-level logging of test type, batch, timestamp and quality checks

The Hub consolidates multiple sensing modes — test strip readouts, vitals monitoring, and imaging — into a single, consistent home-based endpoint.

• Vitals: blood pressure, temperature, oxygen saturation, heart rate, respiratory analysis
• Imaging: wound/skin capture via built-in or companion imaging path
• On-device pre-processing for noise reduction & basic sanity checks

The AegisOne Intelligence Layer spans the Hub and the cloud, handling preliminary scoring, pattern detection, escalation logic, and routing into telehealth, provider, and insurance systems.

• Edge scoring for preliminary results and safety checks
• Cloud-based pattern detection, longitudinal trends & family monitoring
• Routing rules that drive which results go to which clinicians and systems

Test results and vitals are routed to telehealth clinicians for verification, triage, and care planning.

• Configurable queues based on test type, severity, and timing
• Structured summaries for faster clinician review
• Support for prescriptions, care instructions & escalation decisions

Verified results are shaped into provider-ready records aligned with FHIR/HL7 expectations for observations and encounters.

• Mapping of tests and vitals into Observation and DiagnosticReport structures
• Association with existing patient profiles and encounters
• Support for attaching documentation, images, and clinician notes

For insurers and payers, AegisOne focuses on generating structured, claim-ready data that can slot into existing reimbursement workflows.

• Alignment with CPT/ICD coding for test events and diagnoses
• Association between a specific Hub event and billing line items
• Surfaces for utilization review, quality measures & cost models

Core claims target how at-home tests and vitals are orchestrated from the Hub through clinician review into provider systems and payers, rather than isolated algorithms.

• End-to-end workflows from cartridge insertion to verified outcome
• Linking device UX, intelligence logic, and escalation rules
• Integration of diagnostics, vitals, and telehealth decision paths

Some elements are suited to patent coverage; others are deliberately retained as trade secrets to preserve operational asymmetry.

• Patentable device workflows and cartridge interaction patterns
• Trade-secret scoring thresholds, routing rules & safety heuristics
• Documentation that enables clean IP transfer on acquisition

The ecosystem is engineered with an eye toward freedom-to-operate and clean integration into an acquirer's broader device or telehealth portfolio.

• Awareness of existing diagnostic and device IP landscapes
• Clear boundaries between AegisOne logic and commodity components
• Structured artifacts suitable for legal and technical review

The AegisOne Intelligence Layer is designed as decision support, not autonomous diagnosis, with explicit handoffs to licensed clinicians.

• Preliminary results flagged as unverified until clinician review
• Escalation logic for urgent findings and emergency pathways
• Audit trails of clinician decisions and device-originated data

Data handling is designed to align with HIPAA and healthcare security expectations, facilitating smoother review by acquirers' privacy and security teams.

• Encryption in transit and at rest for PHI-bearing data
• Role-based access controls and least-privilege patterns
• Logging for access, device events, and result routing

While regulatory filings will depend on final test panels and deployment models, the device and workflow architecture are developed with FDA pathways in mind.

• Separation of preliminary scoring vs. clinician-verified outputs
• Consideration of device + SaMD roles in potential classifications
• Documentation patterns that support future submission work