AI Solutions for
Healthcare & Medtech
HIPAA-native architecture, HL7/FHIR interoperability, and clinical-grade evaluation — engineered so the system that wins the pilot is the same one still running in production a year later.
Query speed under clinical contexts.
Double-layer protocol at rest & transit.
Network security border enforcement.
100% audit-logged PHI credentials.
AI Solutions for Healthcare & Medtech — The Full Stack
Data flows from the clinic to the point of care through our five-layer architecture. We build robust ingestion systems to ingest raw records, convert them to standardized FHIR formats, and pass them safely across our load-bearing HIPAA Compliance Membrane. Once inside our isolated private inference network, intelligence models execute clinical RAG queries and image analysis, delivering structured summaries to clinician dashboards and medical devices.
Clinical AI Architecture Map
Hover components to inspect pathways; click for technical specifications.
Click Diagram Component
Click any component on the clinical AI architecture map to view technical specifications, integration protocols, and compliance configurations.
Engineered for Clinical and Operational Teams
Our clinical systems are specialized to address the distinct bottlenecks faced by care teams, integration engineers, and digital health founders. We match technology choices directly to these challenges.
Mitigating Charting Fatigue & After-Hours Documentation
Clinicians lose hours daily transcribing dialogue into EHR structures. We deploy specialized ambient audio and NLP layers to convert natural consultation conversations directly into complete, draft SOAP notes.
- check_circle On-device audio preprocessing with local voice activity detection (VAD)
- check_circle SOAP note layout generation using fine-tuned medical vocabulary adapters
- check_circle Explicit human-in-the-loop validation screens prior to EHR injection
Clinical AI Capabilities
Explore our eight core healthcare AI engineering solutions. Click any capability to view the underlying technical specifications and compliance configurations.
Ambient Clinical Documentation
Transcribes patient-doctor consultations, transforming raw audio into structured SOAP notes and draft charting entries.
Clinical RAG & Literature Search
Retrieves evidence from clinical trial files, journals, and local documentation to ground AI responses and check claims.
Medical Imaging Analysis
Executes computer vision inference on medical scan voxel grids to assist radiologists in segmenting tissue structures.
Predictive Risk Scoring
Analyzes patient trajectories to calculate acute risk metrics like sepsis onset or post-operative readmission risk.
Prior Authorization Automation
Compiles clinical records against payer rules to prepare prior authorization submissions and check criteria.
HL7 & FHIR Integration Layer
Standardizes legacy health formats into compliant USCDI resources, bridging data silos without connector drift.
Remote Patient Monitoring Pipelines
Processes real-time Vital telemetry feeds from wearable sensors, alerting care teams when signs deviate.
Medtech Device Software (IEC 62304)
Develops embedded and edge AI solutions under strict Class B/C quality and risk-management principles.
Regulatory & Compliance Command Center
Compliance is the core constraint of clinical AI systems. We design architectures that satisfy standard safety protocols, interoperability rules, and regional privacy mandates.
HIPAA Privacy & Security Rule
Requires strict control over Protected Health Information (PHI) access, complete audit tracking, and administrative, physical, and technical safeguards.
Demands end-to-end database column-level encryption (AES-256), session timeouts, TLS 1.3 in transit, and automatic de-identification scripts running upstream of model entry points.
HITECH Act Guidelines
Expands HIPAA liability, increases penalties for breach disclosures, and mandates strict patient notification timelines in case of data leakage.
Requires deployment of real-time egress network monitors and immutable access logs that cryptographically sign audit actions to establish legally verifiable compliance records.
GDPR Article 9 (Special Category Data)
Prohibits the processing of personal data revealing health, genetic, or biometric status without explicit, informed user consent or legal exemptions.
Forces strict segregation of EU patient records, storage within sovereign European clouds, and model execution paths that guarantee zero data transfer to third-party APIs.
State Health-Privacy Laws (e.g., CCPA/CPRA)
Imposes strict consumer rights over personal health data, including the right to delete, access, and opt-out of automated profiling.
Requires building data deletion pipelines that scrub patient-identifiable vectors from retrieval-augmented indexes without degrading model context structures.
HL7 v2 Message Standard
The legacy event-based messaging standard for transmitting clinical ADT, ORU, and billing events across internal systems.
Demands low-latency MLLP server clusters that parse segment-delimited text payloads, mapping records into structured database schemas in under 50ms.
FHIR R4 / US Core Profiles
ONC-mandated API standard utilizing RESTful JSON endpoints to represent patients, medications, vitals, and conditions.
Requires deploying compliant API controllers validation schemas, supporting search queries and bulk export configurations with SMART on FHIR tokens.
USCDI (US Core Data for Interoperability) v3→v4
Defines the standardized set of health data classes and constituent data elements required for national interoperability.
Constrains model input schemas, ensuring data parsing layers map clinical outcomes to USCDI standards (such as clinical notes and laboratory values).
SMART on FHIR Access Scopes
Integrates OAuth 2.0 authorization flows directly inside EHR interfaces to control external app data read/write capabilities.
Architects client-side user sessions to bind model prompts to active FHIR access tokens, preventing cross-tenant data leaks.
TEFCA Interoperability Framework
The Trusted Exchange Framework and Common Agreement establishes national data-sharing agreements across health networks.
Demands connection gateways to verify credentials of requesting entities before exchanging patient summaries via QHIN interfaces.
EU AI Act Annex III (High-Risk AI)
Classifies clinical decision support and health-monitoring AI as high-risk systems subject to strict post-market oversight.
Mandates building comprehensive risk logs, maintaining quality management documentation, and deploying human-in-the-loop override buttons on diagnostic screens.
FDA Predetermined Change Control Plan (PCCP)
Enables medical software developers to outline planned model retraining parameters and updates without submitting new 510(k) filings.
Requires establishing deterministic model evaluation suites that run on new validation cohorts to check safety margins before releasing weight updates.
ONC HTI-1 DSI Transparency Rules
Mandates source-attribute disclosure for predictive decision support models to evaluate fairness, validity, and calibration.
Demands storing training-set characteristics, validation details, and local calibration statistics directly in metadata indexes accessible to EHR users.
FDA Good Machine Learning Practice (GMLP)
Core guidelines requiring training datasets to represent clinical cohorts, keeping models independent from clinical testing sets.
Enforces strict database partitions that isolate training pipelines from evaluation pipelines, preventing leakage and optimistic calibration bias.
IEC 62304 (Class A/B/C Lifecycle)
The international standard defining software development lifecycle processes for medical device software.
Requires generating traceability reports linking clinical requirements to software units, testing configurations, and anomaly logs.
ISO 13485 Quality Management
Mandates a comprehensive quality management system demonstrating consistent design, development, and delivery of medical devices.
Demands documenting and version-controlling all model training pipelines, pipeline code, and evaluation scorecards under change-control rules.
ISO 14971 Risk Management
Establishes a systematic process for identifying, analyzing, and mitigating hazards associated with medical software.
Requires building fail-safe checks inside workflows, such as fallbacks to human review when model confidence drops below calibration targets.
FDA 21 CFR Part 11 Compliance
Governs electronic records and signatures, requiring closed systems to feature validation trails and automated signature histories.
Mandates storing all clinician confirmations and model evaluations in write-once-read-many (WORM) audit databases.
CMS-0057-F Prior Authorization Rule
CMS mandate requiring payers to build FHIR-based Prior Authorization APIs to streamline authorization turnarounds by 2027.
Forces developers to architect prior authorization models to emit structured FHIR outputs, submitting them via standard payer endpoints.
SOC 2 Type II Security Certification
An operational audit evaluating system security, availability, and processing integrity over an extended observation period.
Demands continuous vulnerability scanning, automated penetration testing, and centralized SIEM alert routing.
EHR & Clinical Systems Integration Matrix
Select an access view below to verify how our integration layer syncs patient data across hospital EMR infrastructures without causing database locks.
| EMR Vendor | FHIR R4 | HL7 v2 (MLLP) | C-CDA Ingestion | SMART on FHIR | Bulk FHIR | Capabilities |
|---|---|---|---|---|---|---|
| Epic (App Orchard) | ✔ Yes | ✔ Yes | ✔ Yes | ✔ Yes | ✔ Yes | All USCDI classes & logs |
| Cerner (Oracle) | ✔ Yes | ✔ Yes | ✔ Yes | ✔ Yes | ✖ No | FHIR resource indexes |
| Athenahealth | ✔ Yes | ✔ Yes | ✖ No | ✔ Yes | ✖ No | USCDI class logs |
| Meditech | ✔ Yes | ✔ Yes | ✔ Yes | ✖ No | ✖ No | Demographics & vitals |
| eClinicalWorks | ✔ Yes | ✔ Yes | ✖ No | ✖ No | ✖ No | FHIR observations |
| Legacy Systems | ✖ No | ✔ Yes | ✔ Yes | ✖ No | ✖ No | Database triggers / ETL |
Clinical Reference Architectures
Select an architectural block to view corresponding config schemas and security rules designed for medical technology infrastructure.
# De-identify PHI before sending to LLM
deid_client = DeIDGateway(endpoint=VPC_ENDPOINT)
clean_prompt = deid_client.redact(raw_clinical_notes)
# Execute local clinical model query
response = clinical_model.generate(
prompt=clean_prompt,
temperature=0.0,
max_tokens=512
)Custom Build Milestones
We guide systems from initial regulatory mapping to continuous deployment using five development milestones.
Clinical & PHI Discovery
We perform an initial audit of clinical workflows and model requirements, constructing a complete PHI access map.
Compliance Architecture Configuration
We provision secure, encrypted datastores (AES-256) inside air-gapped private VPCs, configuring audit ledger systems.
Encrypted Pipelines & Interoperability
We connect patient database feeds using compliant FHIR APIs, deploying our boundary de-identification gateways.
Model Fine-Tuning & Evaluation
We train models on anonymized health datasets, testing accuracy metrics against clinical consensus standards.
Production Launch & Drift Telemetry
We deploy our validated models into production behind VPC firewalls, monitoring for weight drifts and latency issues.
Clinical Safety Boundaries & Assistive Operations
All AI systems built for health networks operate strictly as assistive utilities. They summarize records, retrieve trial files, and highlight scan regions to save doctor time, but do not make autonomous diagnostic recommendations.
Our software enforces human-in-the-loop validation checkpoints. Before any AI output is injected back into a patient record, a certified physician must review, correct, and electronically sign the entry.
Clinical Evaluation & Safety Harness
Unlike standard language models, clinical AI must operate within bounded parameters. We evaluate models against five safety dimensions prior to deployment.
Forces generation algorithms to only use parsed, retrieved documents from local journals and EHR histories.
Aligns model outputs with standard care guides, checked using automated prompt evaluation filters.
Balances true positive and false positive rates under strict medical professional guidance.
Matches prediction confidence values directly to actual historical outcome statistics.
Validates model outcomes across patient demographic categories to maintain parity.
Curated Healthcare AI Toolchain
We build on top of specialized models, event stream brokers, database engines, and access tools configured for clinical compliance.
Cost & Timeline Reality Check
Estimate target durations and required compliance files for clinical software projects using our scoping framework.
Technical Scoping & Estimator
Select your architectural requirements to view target timelines and required compliance deliverables.
Frequently Asked Questions
Review details concerning clinical AI development timelines, compliance regulations, and integration techniques.
What does a healthcare AI development company actually build?
A healthcare AI development company builds secure, HIPAA-compliant machine learning pipelines, structured data ingestion systems, and clinician assist interfaces.
This includes transcribing ambient consultations, querying clinical trial databases, segmenting medical imaging anomalies, and automating prior authorizations. All solutions are tailored to fit client VPC networks.
How do you protect PHI in an LLM pipeline?
We protect Protected Health Information (PHI) by running de-identification filters upstream of any model call.
This de-identification gateway extracts and redacts Safe Harbor 18 identifiers, replacing clinical entities with secure token mappings inside isolated private cloud environments to prevent unauthorized data egress.
Can you integrate with Epic, Cerner, or Athenahealth?
Yes. We integrate with major EHR systems using standard HL7 v2 messaging streams, RESTful FHIR R4 APIs, and SMART on FHIR authorization protocols.
This ensures bidirectional data syncing without causing database locks, maintaining consistency across patient records.
Are your healthcare AI models diagnostic?
No. Our systems are strictly assistive tools designed to summarize notes, search literature, and segment scans.
All outcomes must be verified by a licensed clinician, preserving human-in-the-loop clinical supervision gates to prevent errors and ensure safety.
Does the EU AI Act apply to clinical decision support software?
Yes. Under the EU AI Act, clinical decision support software is classified as Annex III high-risk AI.
This classification mandates strict compliance schedules including quality management audits, risk logs, and validation trials before deployment in EU markets.
What is a Predetermined Change Control Plan (PCCP) and when do we need one?
A Predetermined Change Control Plan (PCCP) is an FDA-approved document detailing how an adaptive AI/ML medical device will be retrained and updated in production.
A PCCP is required when developers plan to modify model weights and parameters dynamically without submitting new 510(k) clearances.
How does CMS-0057-F change prior authorization engineering?
CMS-0057-F mandates that payers implement FHIR-based APIs to handle prior authorizations by 2027.
Developers must build clinical models that output structured data matching these FHIR exchange schemas, allowing automated prior authorization submission.
What is the difference between HL7 v2 and FHIR, and which do we need?
HL7 v2 is a segment-delimited legacy messaging system used for real-time internal triggers, while FHIR is a RESTful JSON standard used for structured interoperability.
Modern clinical integrations typically require both systems: HL7 v2 to capture real-time clinic events, and FHIR to read and write detailed structured records.
How long does it take to ship a HIPAA-compliant AI system to production?
A standard production-grade HIPAA-compliant clinical AI system takes 12 to 16 weeks to build.
The exact timeline depends on whether the system requires direct EHR integration, multi-vendor interoperability layers, or specialized regulatory clearance documentation.
Do you build software for medical device companies (IEC 62304)?
Yes. We develop software following IEC 62304 Class B/C guidelines.
We compile quality documentation, ISO 14971 hazard evaluations, and traceability reports to support regulatory clearance submissions for hardware and software devices.