Trust intelligence for healthcare AI
Healthcare AI has life-or-death stakes. Deploy AI with the trust, safety, and compliance that patients and regulators demand.
Healthcare AI is different
Healthcare AI has life-or-death stakes. Most AI tools aren't ready for healthcare's trust requirements.
Hallucination risk
AI confidently recommending wrong treatments. In healthcare, confident mistakes can be fatal.
Bias and equity
Models trained on unrepresentative populations. Healthcare AI must serve all patients fairly.
Drift over time
Performance degrades as patient populations change. What worked last year may not work today.
Opacity problem
Clinicians can't understand or verify AI reasoning. And they shouldn't trust what they can't verify.
Where healthcare AI fails
These failure patterns appear repeatedly in healthcare AI deployments. Understanding them is the first step to preventing them.
Overconfident wrong answers
LLMs don't say "I don't know." They confabulate with authority. A confident wrong diagnosis is worse than no AI at all. AI scribes confidently document things that weren't said — a hallucinated allergy becomes part of the permanent record. Confidence calibration is essential.
Silent drift
Performance degrades gradually. No alarm bells. Vendor model updates change behavior without notice. By the time someone notices, months of suboptimal care have occurred. Continuous monitoring catches drift before harm accumulates.
Dataset bias
Trained on academic medical center populations. Performs poorly on community hospital patients. Skin cancer detection that only works on light skin. Sepsis prediction calibrated for ICU populations. Test across your actual patient population.
Liability transfer
Who's responsible for AI-generated documentation? The physician who signed it. "The AI did it" isn't a defense. Context window limitations mean AI misses critical history. Clinicians need tools to verify AI output before attestation.
Guardian monitors for hallucination and drift. Eval validates against clinical standards. Protect clinicians and patients from AI failures.
The FDA hasn't cleared LLMs for clinical decisions
Zero large language models have received FDA clearance for clinical decision-making. Understanding the regulatory landscape is essential.
SaMD pathway
Software as a Medical Device requires clinical evidence, quality management systems, and ongoing post-market surveillance. LLMs face unique challenges: non-deterministic outputs, inability to fully characterize behavior, continuous model updates from vendors.
EU MDR requirements
Medical Device Regulation in Europe has strict requirements for clinical evidence, notified body review, and post-market monitoring. AI-specific guidance emphasizes transparency, human oversight, and bias testing.
Medical licensing frameworks
Medicine is licensed at national and regional levels — state practice acts in the US, GMC in the UK, medical boards across the EU. AI that "practices medicine" may violate licensing laws. Careful positioning as clinical decision support — not autonomous diagnosis — is essential.
Evolving regulatory landscape
Regulatory approaches are shifting globally — some jurisdictions tightening, others easing requirements. But liability doesn't disappear with deregulation. Malpractice, product liability, and institutional credentialing still matter everywhere. Trust infrastructure protects you regardless of regulatory changes.
Trustworthy AI across the care continuum
From clinical decision support to patient engagement, the Trust Intelligence Platform provides the infrastructure for healthcare AI you can verify.
Clinical Decision Support
Diagnostic support, treatment recommendations, drug interaction checking, and risk stratification — all with explainable reasoning, confidence levels, and citations. Full audit trail for M&M review and liability protection.
Products: Guardian, Eval, Steer
AI Scribes & Documentation
Ambient clinical intelligence that frees clinicians from documentation burden. Guardian monitors for hallucination in real-time. Continuous validation against clinical standards. Protect clinicians from signing off on AI-generated errors.
Products: Guardian, Eval
Patient Digital Twins
Continuous physiological modeling from wearables, labs, and vitals. Predictive disease progression. Treatment optimization. Personalized care at scale — but only valuable if accurate. Guardian monitors prediction accuracy against clinical outcomes.
Products: Guardian, Orchestrate
Patient-Facing AI
Symptom checkers, scheduling, care navigation, health coaching. Safety-first design: never provide dangerous medical advice. Appropriate boundaries with human handoff for clinical decisions. Privacy-compliant personalization (HIPAA, GDPR, regional requirements).
Products: Guardian, Steer, AgentOps
Multi-Agent Clinical Orchestration
Complex clinical decisions often require multiple perspectives. Multi-agent patterns mirror clinical team dynamics — when used appropriately. Orchestrate manages coordination with clinical-grade reliability.
Sequential handoff
Triage agent assesses urgency. Specialist agent provides domain expertise. Summary agent synthesizes recommendations. Each agent builds on prior work. Use for: initial assessment workflows, referral triage, care coordination.
Parallel consultation
Multiple specialist agents analyze simultaneously. Cardiology, nephrology, pharmacy perspectives in parallel. Synthesis agent integrates recommendations. Use for: complex patients with multiple comorbidities, polypharmacy review.
Hierarchical escalation
Junior agent handles routine cases. Escalates uncertainty to senior agent. Senior agent can override or confirm. Use for: high-volume screening, triage optimization, quality assurance.
Adversarial review
Recommender agent proposes treatment. Challenger agent argues alternatives. Evaluator agent makes final determination. Use for: high-stakes decisions, treatment plan validation, safety checks.
AI for healthcare operations
Beyond clinical care, healthcare organizations deploy AI for revenue cycle, patient access, and administrative efficiency. Trust Cascade for prior authorization alone delivers 70% auto-adjudication and 40% cost reduction.
Revenue cycle
Prior auth automation with Trust Cascade — rules for straightforward approvals, ML for pattern-based decisions, AI for complex medical necessity. Plus coding assistance, denial management, and claims optimization.
Products: Orchestrate, Guardian
Patient access
Scheduling optimization, capacity management, no-show prediction, wait time reduction. AI that improves access while maintaining trust.
Products: Guardian, Steer
Administrative AI
Document processing, patient communication, staff scheduling, supply chain optimization. Guardian monitors scheduling AI for drift and tracks coding accuracy over time.
Patient 360
Unified view across EHR, claims, and social determinants. ETL-C provides contextual understanding of patient journey. Real-time data for operational decisions with semantic joins across disparate systems.
Clinical AI trust across 12 hospitals in 14 weeks
A multi-site health system across 12 hospitals was deploying AI scribes to 400+ clinicians but had no framework for monitoring documentation accuracy. Three months in, a hallucinated drug allergy nearly caused a treatment delay. They needed trust infrastructure — fast.
Audited 6 clinical AI systems including scribes, CDS tools, and patient chatbots. Mapped trust gaps — zero hallucination monitoring, no bias testing, inconsistent audit trails. Established baseline accuracy metrics.
Deployed Guardian across all scribe instances. Integrated Eval for continuous validation against clinical documentation standards. Built real-time alerting for hallucination detection and documentation errors.
Trained clinical informatics team and 12 site champions. Ran tabletop exercises for AI documentation failures. Built escalation procedures for flagged notes.
Full rollout with continuous monitoring. Provider adoption increased 40% once clinicians could see trust metrics. Passed accreditation review with full AI documentation audit trail.
Related reading
Deep dives from our team on the topics that matter most.
Healthcare data is uniquely hard
Healthcare data integration challenges make AI deployment difficult. "Blood pressure" is represented 17 different ways. AI needs normalized, semantic data — not raw feeds.
FHIR, HL7, CDA
Multiple standards, inconsistent adoption. FHIR is the future but HL7v2 is the present. Integration requires understanding all of them.
EHR fragmentation
Epic, Cerner, Meditech, athenahealth. Each with different APIs, data models, and integration patterns. Patient data scattered across systems that don't talk to each other.
ETL-C solution
Context-first processing that preserves clinical meaning. Semantic normalization across sources. AI-ready data that maintains provenance and relationships.
Healthcare Data Intelligence
AI is only as good as the data it has access to. Our Data Intelligence capabilities make your healthcare data AI-ready.
Healthcare Data Engine
Deploy on your preferred cloud infrastructure. Native integration with Google Cloud Healthcare API, AWS HealthLake, or Azure Health Data Services. HIPAA and GDPR-compliant data processing from day one.
ETL-C for Clinical Data
Context-first processing for EHR, claims, and SDOH data. Preserve clinical context that AI needs to reason correctly. Reduce hallucination by giving AI the full picture.
SARP for Healthcare Scale
Agent-ready data platform that handles AI query volumes. Semantic APIs for clinical concepts, not just tables. Sub-second response times at 100x the query volume of traditional BI.
Built for healthcare compliance
Our solutions support healthcare's complex regulatory landscape from day one.
Health data privacy
HIPAA (US), GDPR (EU), PIPEDA (Canada), and regional health data regulations. PHI/PII protection in all processing. Audit logs for compliance verification across jurisdictions.
Medical device regulation
Model documentation for SaMD (Software as Medical Device). Support for FDA (US), EU MDR, TGA (Australia), and PMDA (Japan) submission requirements and post-market surveillance.
Interoperability standards
FHIR, HL7, and IHE compliance. Support for national interoperability requirements including ONC (US), NHS Digital (UK), and regional health IT standards.
Equity & Fairness
Bias testing for equity requirements. Fairness monitoring across patient populations. Continuous validation, not point-in-time checks.
Start your healthcare AI journey
Healthcare AI Assessment
$35K
3 weeks. Current AI inventory and risk assessment. Compliance gap analysis. Use case prioritization. Trust architecture recommendations.
Clinical AI Pilot
$100K
8-10 weeks. Implement Guardian + Eval for one clinical use case. Demonstrate trust metrics improvement. Build clinician confidence and adoption.
Enterprise Healthcare Platform
$400K+
6-9 months. Full trust infrastructure deployment. Integration with EHR and clinical systems. Clinical and technical team enablement.
Customer success stories
See how organizations like yours have deployed AI with trust.
Healthcare AI that clinicians actually trust
Life-or-death stakes demand AI you can verify. Every recommendation explainable. Every system monitored.