WHAT PONTEGRA PROVIDES
Design, deploy, and manage SMART-on-FHIR apps and CDS services
Pontegra helps healthcare organisations deliver clinical decision support through SMART-on-FHIR applications and CDS Hooks–compliant services.
We enable guideline-based recommendations, operational safety controls, localisation, and feedback loops, so decision support works in real clinical workflows, not in isolated dashboards.
Powered by: KronIQ (pathways) + onfhir-cds (CDS services) + Repofyr (FHIR repository/API) + Ignifyr (HL7 FHIR® integration).
WHAT YOU CAN DELIVER
What you can deliver
Clinician-facing SMART-on-FHIR apps
- EHR-embedded apps launched in patient context
- Task-focused workflows (screening, monitoring, escalation, referral)
- Shared care plan views and structured data capture where needed
CDS Hooks services (guidance that appears at the right moment)
- “Cards” and suggestions triggered by clinical workflow events
- Prefetch-based context retrieval for fast, relevant recommendations
- Configurable rules and content that can be adapted per site/region
Patient empowerment experiences (when required)
- Patient-facing SMART apps using reusable UI components and reference designs
- Support for self-monitoring, education, and care plan adherence
Why choose PONTEGRA
AI-enabled decision support for prediction and simulation in workflow
Many organisations are building predictive models (risk scores, deterioration prediction, readmission risk) but struggle to operationalize them inside the EHR in a safe, governed way.
Pontegra helps integrate AI capabilities into clinical workflow by wrapping prediction and simulation services as decision support services:
AI prediction integration (risk scoring)
- Invoke a model service to generate a risk score or predicted outcome
- Return results to clinicians via CDS Hooks cards and/or inside a SMART app
- Capture “why this fired” with traceable context (model version, timestamp, input summary)
Simulation / “what-if” support
- Expose simulation services (e.g., projected outcomes under different interventions) behind a controlled interface
- Present outputs as guidance, not opaque scores—linked to next actions and clinical reasoning
Governance-minded operationalization
- Ensure each model invocation is logged and traceable
- Support review and iteration (feedback capture, rule tuning, threshold calibration)
- Enable site-by-site configuration and language localization
HOW PONTEGRA MAKES THIS PRACTICAL
How Pontegra makes this practical
1.
Configurable content and localization
- Site-specific medical terminologies, thresholds, formularies, pathways, and escalation rules
- Multilingual content templates and consistent rendering of guidance
2.
Secure operations patterns
- Controlled access to FHIR data sources
- Safe output patterns and audit-friendly run/event logging
- Compatibility with secure environments (SPE/TRE-style deployments)
3.
Feedback loops to improve quality
- Track when guidance is shown, accepted, dismissed, or overridden by practitioners
- Feed insights back into rule tuning, pathway refinement, and model recalibration
WHERE THIS FITS
Where this fits
Hospitals and EHR programs
- embedded decision support for guideline adherence and safety checks
- structured follow-up pathways (e.g., diabetes monitoring, CKD surveillance)
National / regional digital health programs
- configurable guideline packages and localization
- consistent operational controls and quality management
AI teams who need clinical adoption
- model integration into daily clinical workflows (not just AI notebooks)
- controlled deployment patterns and traceability
Our packages and pricing options will be published soon…