Workflow Demo
This page shows one grounded, early-stage pathway: a patient reports symptoms, QbitQure preserves the source record, projects the event into FHIR, and makes the result reviewable through a provenance-aware workflow.
A patient reports ongoing fatigue, poor concentration, and intermittent dizziness. In a mature precision-medicine setting, that kind of intake might later be combined with history, biomarkers, or other longitudinal data. At this stage, QbitQure shows the groundwork: capture the information cleanly, preserve it, and move it into an interoperable structure.
Personalised medicine needs trustworthy data flow before advanced models can add value. This workflow demonstrates source retention, FHIR transformation, retrieval, and auditability, which are all prerequisites for later decision support or more advanced computational methods.
Step 1
A patient submits a short symptom summary through the intake form, such as persistent fatigue and dizziness.
QbitQure validates the submission, records the raw source payload, and attaches basic metadata such as receipt time and provenance information.
Step 2
The original source record is stored so the platform can preserve what the patient actually reported before any transformation happens.
That gives the system an audit trail and supports later review of where downstream FHIR resources came from.
Step 3
The same intake event is projected into interoperable clinical structures such as Bundle, Patient, Encounter, Consent, and Observation.
This is the layer that makes the data more reusable for integration, retrieval, and future decision-support workflows.
Step 4
A reviewer can inspect the generated FHIR record set and, where available, navigate back to the raw intake source that produced it.
That creates a more trustworthy path from patient-entered information to structured data and eventual personalised-medicine workflows.
Patient-entered data is not left as app-only JSON. It is pushed into FHIR-oriented structures.
Reviewers can reason about where the structured data came from and trace it back toward the source intake record.
This is the kind of workflow substrate that later supports explainable decision support, stratification, and more advanced computation.
QbitQure can now also show a thin rules-based review suggestion on top of this workflow, with the matched symptom signals exposed clearly rather than hidden in a black box. It also shows a clinician review state so the recommendation sits inside a governed workflow instead of appearing autonomous.
Intake capture, source retention, FHIR projection, and bounded rule-based prioritisation can be automated because they organise and prepare the case without owning the final clinical decision.
Confirming urgency, interpreting the case in context, and recording the review outcome remain clinician-led because they carry judgement, responsibility, and accountability.
Routine review pattern
Shows how QbitQure can surface a non-urgent but clinically useful review suggestion with visible reasoning.
Open this scenarioUrgent neurological pattern
Shows how the same explainable layer can surface a clearly higher priority review recommendation with matched red-flag signals.
Open this scenario