Symptoms happen
continuously.
Care still sees snapshots.
MedGuardian turns symptom logs and wearable-style vitals into structured clinical timelines, consent-gated access, AI-supported doctor briefs, and auditable workflow receipts.
See the MedGuardian system in motion.
This is the full product presentation from the actual repo build: clinical timeline, consent flow, AI-supported review, and receipt-backed workflow coordination in one continuous walkthrough.
Structured symptom history, drift, and context brought into one usable clinical view.
Approval gates and redaction rules stay visible in the workflow instead of hiding behind claims.
Chainlink-linked workflow evidence, cost telemetry, and traceable actions shown in-context.
Healthcare loses signal between appointments.
Patients experience symptoms over days, weeks, and months. Clinicians often must make decisions from short visits, partial recollection, scattered messages, and isolated readings. That gap is where risk gets missed.
MedGuardian closes that gap by turning ongoing patient input into structured clinical context without giving up privacy or accountability.
One system. Four layers of trust-aware care intelligence.
From symptom log to accountable care action.
Privacy here is not a feature.
It is the architecture.
MedGuardian separates raw vitals from what the interface reveals, introduces consent before exposure, links sensitive events into an audit chain, and produces receipts for workflow accountability.
The receipt model, in detail.
Every workflow action surfaces cost metadata, proof hashes, and consent records. Inspectable, not just stated.
Human validation,
overflow care,
and tracked handoff.
The Professional Network is MedGuardian's collaborative care marketplace. When AI detects a case that needs human judgment, the platform turns that signal into structured tasks for doctors, nurses, lab staff, caregivers, and specialists, with claim, review, approval, and payout tracking built in.
MedGuardian does not stop at “an alert was detected.” It packages the escalation into accountable human work: patient ID, severity, symptom notes, feature signals, triage summary, rationale, confidence score, escalation level, and required roles.
Capacity, validated cases, open tasks, and rewards paid shown as the network's operating surface.
Select a working professional, inspect their load, and manually intake a new case into the network.
A demo-friendly flow across Intake, Claim, Submit, and Approve + Pay, with role-specific task ownership.
Live case creation, claims, submissions, approvals, validations, and payout events reflected over WebSockets.
Always included. Owns clinical validation, signs off on judgment, and can receive scoped Doctor Portal access the moment a doctor-role task is claimed.
Joins moderate-or-higher cases to coordinate follow-up, handle adherence-oriented review, and keep the triage loop moving when care work becomes operational.
Added for higher-acuity cases where supporting tests, specimen workflows, or evidence review need to be attached to the escalation package.
Supports lower-severity, fatigue, recovery, and adherence cases where real-world context and continuity matter as much as the signal itself.
Added when glucose drift, metabolic symptoms, meal patterns, or nutrition context appear inside the reason for escalation or triage summary.
Cases can enter manually from the UI, from simulation events, or from an Akasha escalation when risk crosses the threshold for human review.
Every case stores patient ID, severity, symptom notes, feature signals, rationale, confidence, escalation level, and the exact human roles required to resolve it.
Tasks are created per role with title, priority, due time, claimant, and submission data. Reviews include notes, confidence, recommendation, follow-up actions, and evidence.
Approved work moves the case toward validated state, can trigger demo payouts, and leaves behind a ledger of who acted, what they submitted, and when it was approved.
When a pooled doctor claims a doctor-role task, MedGuardian grants scoped patient access through the Doctor Portal instead of bypassing consent. Human review and privacy enforcement remain part of the same system.
Tested against hard-to-track patient realities.
MedGuardian is designed for longitudinal complexity, not idealized single-event demos.
Better care needs more than intelligence. It needs trust.
Healthcare AI often fails in predictable ways: patients do not trust where their data goes, clinicians do not trust opaque recommendations, and teams cannot prove what happened inside the workflow.
MedGuardian is built to make the flow inspectable, consent-aware, and accountable. Not just intelligent, verifiably trustworthy.
See a privacy-preserving clinical workflow,
not just another AI demo.
Explore how MedGuardian turns symptom timelines, clinician review, professional task routing, Chainlink-audited workflows, and verifiable receipts into one accountable care coordination system.