AI-Powered Clinical Workflows
FLS believes that AI-powered clinical workflows coupled with the FHIR strategy that underpins your data layer is a formula that will create the desperately needed efficiency gains in the healthcare sector. FHIR is pathway AI helps you create the value.
What drives successful clinical AI projects in healthcare organisations?
Healthcare organisations are now open to a healthy AI strategy. Here some of the ingredients need for success.
AI without the right data is noise – Enter Interoperability
Data must flow and be structured before it can be intelligent. The organisations seeing real AI outcomes built the FHIR foundation first — then layered intelligence on top of it.
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Reduction in clinical documentation time per clinician
A global engineering team building and running AI-native systems for enterprises in regulated and complex industries.
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Client Retention Rate
Per clinician per day returned to patient care
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Levels
Clinical safety governance — human-in-the-loop at every stage
What AI workflows does FLS deploy on top of FHIR data?
Three categories of workflow — built by FLS through Clinovera — running on FHIR-native data. Human-in-the-loop design at every stage.
| Workflow | What It Automates |
|---|---|
| Documentation Automation | Ambient AI listens to clinical conversations and generates structured notes automatically — mapped directly to FHIR resources. The clinician reviews and approves every output before EHR entry. Nothing enters the record without human sign-off.50%+ reduction in documentation time. 30–60 minutes per clinician per day returned to patient care. |
| Clinical Analytics | AI-ready data assets built on FHIR-standardised pipelines. Clinical decision support at the point of care: allergies, diagnoses, and medication interactions surfaced from real-time FHIR queries.Drift detection, anomaly detection, population health, and real-world evidence analytics on OMOP, i2b2, and FHIR data. |
| Patient Engagement Tools | Compliant patient record access built on FHIR APIs. Natural language eligibility queries on raw EHR data. Prior authorisation automation with payer-provider FHIR pipelines — reducing admin burden and approval cycle time.Patient portal development with sovereignty and security by design. |
Built for clinical and technology leaders managing the intersection of AI and care.
The roles that carry the most accountability when clinical AI either delivers or fails.
Decision Makers
— CMIO & CMO
— CIO, CTO & IT Directors
— Head of Clinical Informatics
— Chief Nurse Informatics Officer
Built For These Organisations
— Regional hospitals & health systems
— Commercial healthcare vendors
— Digital health companies & startups
— R&D & university health systems
How are AI outputs validated for clinical safety?
Safety is not a feature added to clinical AI — it is the architecture it is built on. Every Clinovera workflow operates under a four-level governance framework.
Model governance
Validated against clinical benchmarks. Monitored continuously for drift, degraded performance, and output reliability.
Human-in-the-loop
No AI output enters a clinical system without clinician review and approval. Clinical judgement is never removed from the process.
Compliance validation
Outputs checked against CMS rules, quality metrics, and organisational standards before surfacing in clinical systems.
ISO 27001-aligned infrastructure
Full data lineage, audit logging, access controls. Secure, compliant audio processing for ambient documentation with explicit patient consent.
Powered by InterSystems IRIS Managed AI and Embedded Python. Built on top of the FHIR Integration & Modernization foundation.
Regional compliance depth is our core differentiator
Regulatory requirements are not obstacles — they are the buying trigger. FLS delivers with local compliance built in from day one across all target markets.
Australia
- National FHIR adoption (Sparked AU)
- Data sovereignty requirements
- My Health Record integration
UK
- NHS interoperability standards
- GDPR (data sovereignty)
- European Health Data Space
- Target commercial vendors — not NHS directly
United States
- HIPAA compliance
- ONC FHIR mandates for payers
- 21st Century Cures Act
- Requires local partnership; longer entry timeline
From the field
A real engagement. Fill in the fields below before publishing.
| Client | What was built | Outcome |
|---|---|---|
| Skilled Nursing Facilities network | GenAI intake pipeline for multi-format referrals (fax, PDF, EMR). Extracts clinical data, generates NTA scores and red-flag alerts for admission decisions. Integrated into Smart Admissions platform via custom API. | Faster admissions workflows, reduced staff burnout, smarter placement decisions, stronger financial outcomes. Deployed at multiple SNFs. EHR integration underway for fully automated referral-to-admission. |
| Rare disease registry platform | OMOP CDM platform aggregating EHR data from FHIR, HL7, CCDA, and proprietary sources. Intelligent patient matching, data profiling, multi-tenant Azure cloud. Built on InterSystems HealthConnect. | Production-grade registry platform across 3 continents. Clean OMOP CDM data enabling real-world evidence research. Extensible architecture supporting disease-specific data models. |
| Academic medical center | Azure AI image recognition to tag non-compliant EHR employee photos. Admin web app (React / .NET) for human review and confirmation. Human sign-off built into every step. | Significantly improved EHR image quality at scale. Efficient sorting of compliant vs non-compliant images at volume — previously impractical to do manually. |
What clinical and technology teams ask first
Do we need FHIR interoperability in place before starting AI workflows?
In most cases, yes. AI on top of fragmented, inconsistent data produces unreliable clinical outputs. FHIR provides the structured, standardised data foundation that clinical AI models require. Documentation automation, clinical analytics, and patient engagement tools all perform significantly better — and more safely — on a FHIR-native data foundation. If your systems are not yet on FHIR, the FHIR Integration & Modernization engagement is the right starting point.
How does documentation automation work without compromising clinical accuracy?
The AI listens to the clinical conversation with explicit patient consent and compliant audio processing, then generates a structured note mapped to FHIR resources. The clinician reviews and approves every output before it enters the EHR. Nothing is written to the record without human sign-off. The workflow reduces administrative time without removing clinical judgement from the process.
How are AI outputs validated for clinical safety?
Safety operates at four levels: model governance (validated against clinical benchmarks, monitored for drift), human-in-the-loop design (no output enters a clinical system without clinician review), compliance validation (outputs checked against CMS rules, quality metrics, and organisational standards), and ISO 27001-aligned infrastructure with full data lineage, audit logging, and access controls.
Where do teams typically start?
Most start with documentation automation — it has the most immediate and measurable impact on clinical time. Teams with active analytics requirements often start with the clinical analytics layer. Patient engagement tools and prior authorisation automation are typically introduced in the second phase once the core AI layer is stable and validated.
What is Clinovera and what is its relationship to First Line Software?
Clinovera is the dedicated healthcare AI division of First Line Software. All AI-powered clinical workflow engagements are delivered by FLS through Clinovera — combining FLS’s engineering execution with clinical domain expertise and healthcare-specific AI governance.
Which workflow would you automate first?
Most teams start with documentation automation — it returns measurable clinical time on day one. We scope the first workflow in a single 45-minute call.
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