Industry: Healthcare / Autonomous Care Management

Challenge: Emergency Response Requires Intelligence at the Point of Care

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Red Rock Robotics, part of Red Rock Group Holdings, operates a robotic, locally AI-driven, autonomous care management platform that continuously monitors patients across multiple facilities, detecting abnormal vitals, falls, mobility changes, and behavioral anomalies in real time through contactless sensing and autonomous rounding. Red Rock Robotics employs patented and FDA-approved technologies to effect care. When a patient experiences a cardiac irregularity, sudden fall, or respiratory change, every second counts. In emergencies, the difference between a clinical response in seconds versus minutes determines whether a patient survives with full function or suffers permanent neurological damage.

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This platform generates high-frequency, facility-distributed clinical signals, but delivering those insights to the right clinician with full context, while maintaining HIPAA compliance, strong data security, and coordinated access across clinicians, caregivers, and operators, presents a challenge that traditional healthcare architectures cannot solve. In practice, the challenge isn’t just detection, but also governed action: ensuring alerts route to the right person, escalate if unanswered, and do so with identity, consent, and auditability built in by design.

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Healthcare facilities face a fundamental tension: autonomous monitoring produces critical intelligence at the point of care, but clinical decision-makers are geographically dispersed, access patient records through different systems, and operate under strict regulatory constraints that prohibit centralizing protected health information (PHI).

Current approaches fail on multiple fronts:

• Clinical teams operate with fragmented data. Observations are often isolated from the EMR context, requiring manual synthesis under time pressure
• Centralizing patient data to enable AI analysis increases HIPPA risk and violates data sovereignty requirements 
• Traditional architectures force a false choice: share data to enable better AI (violating compliance) or maintain security by limiting insights (reducing care quality)
• Every delay in information delivery increases clinical risk, especially during   emergencies
• Facility-specific integrations don’t scale, driving up cost and operational complexity
• Compliance burden multiplies when PHI moves between systems, each movement creates new audit, encryption, and access control requirements
• Workflows that isolate or silo events trigger pockets of accrued risk and liability.  

Federated AI That Brings Intelligence to the Point of Action

Rather than attempting to centralize patient data, Red Rock Robotics employed Axonis to deploy a federated AI architecture that allows AI models to be trained and executed where data already lives within facility systems, the autonomous care platform, and Electronic Health Record (EHR) environments.

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In partnership, Axonis Federated AI and Ekko (identity-proofing technology and consent controls from the Red Rock Group Holdings family) enable “actionable intelligence” to move without moving raw Protected Health Information. Compliance becomes programmable, enforced through policy-driven APIs rather than brittle, manual processes.

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With Axonis Federated AI:

• Each institution retains full ownership and control of patient data. Nothing moves without explicit authorization
• AI is moved to the data, instead of pulling sensitive information into centralized platforms
• Intelligence is applied locally, in real time, at the point of clinical decision-making
• Stateless orchestration (via Ekko) coordinates insights across distributed systems without centralizing PHI
• Complete audit trails prove who accessed what data, when, where, and why — satisfying HIPAA requirements. Ekko will be the first to comply to the recent NIST 800-63-4 standards released in December of 2025.
• Identity and consent policies determine alert routing and access, including clinicians, caregivers, and authorized proxies
• Identity and consent determine who can receive an alert, view context, and participate in care coordination, including authorized caregivers and proxies.

This architecture enables secure, real-time collaboration across distributed care networks without exposing raw patient data or disrupting existing operations.

Delivering Clinical Intelligence While Maintaining Data Sovereignty

Red Rock Group Holding and Axonis AI are working with a leading US Healthcare Provider to roll out this solution, servicing 100,000 beds across over 500 sites to securely deliver decision intelligence across their care facilities.

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Using Axonis Federated AI deployed across their autonomous care platform and facility EMR systems, the organization is transforming how clinical intelligence flows to the point of care.

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When the local facility detects a high-risk critical event, federated AI immediately synthesizes insights and routes alerts to the appropriate clinician, enriched with context including vital trends, patient baselines, and relevant medical history. All analysis occurs where the data resides. Intelligence is delivered in seconds, and PHI never leaves the facility systems. 

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Critically, the workflow supports policy-driven escalation. If an alert is not acknowledged, the system automatically routes to the next authorized responder, reducing delays and lowering clinical and operational risk.

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At the moment an alert is received, clinicians access contextual, decision-ready insights, including:

• Vital sign trends with historical baselines
• Medication and treatment history from EMRs
• Prior similar events and documented outcomes
• Consent-driven access indicators showing authorization rationale

Instead of requiring clinicians to manually gather information from multiple systems or wait for data consolidation, decisions are made immediately and locally with complete context. Physicians make more informed decisions faster. Nurses coordinate care with real-time patient status. Family members monitor loved ones remotely with appropriate permissions. All interactions create complete audit trails, proving legitimate access and satisfying HIPAA requirements.

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In the US, identity is both a privacy and patient-safety issue.  Without a universal national patient identifier, fragmented records contribute to medication errors and missing allergy context. Identity-first workflows help close these gaps, improving safety while preserving privacy. 

Clinical Significance and Operational Impact

In emergency medicine, outcomes are measured in seconds. A septic patient treated with antibiotics within the first hour has dramatically higher survival rates than one treated two hours later. A stroke patient receiving treatment within three hours has the possibility of full recovery. After four hours, neurological damage becomes permanent. A cardiac patient reaching defibrillation in minutes versus tens of minutes determines survival with full brain function.

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By enabling federated AI to synthesize intelligence at the point of care and deliver it in seconds without delays inherent in centralizing data, health providers can directly improve clinical outcomes. Seconds and minutes are the difference between recovery and permanent disability, between life and death.

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Beyond emergency response, the federated approach delivers strategic advantages:

• Better care with existing staff. Autonomous monitoring augmented by federated AI insights enables more informed decisions without additional physicians or nurses
• Cost efficiency. No expensive data lake infrastructure. No cloud consolidation projects. Lower total cost of ownership than centralized approaches
• Regulatory confidence. HIPAA compliance built into architecture. Data sovereignty maintained. Audit trails prove legitimate access
• Zero-trust principles reduce exposure from breaches and AI-related data leakage.
• Scalability across facilities. The same architecture deploys at each facility without requiring data sharing agreements. Each facility contributes to federated learning models

Pilot and Rollout: Q1 2026 through 2026

Initial deployment across one facility validates the architecture in production healthcare, refines clinical workflows with real patient data and care teams, and demonstrates HIPAA-compliant federated operations. Accelerated expansion across all facilities throughout 2026 adds facility data to federated learning models, improving insight accuracy across the network while maintaining data sovereignty at each institution.

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Rather than a traditional vendor implementation, this partnership is a co-creation. Axonis designed federated AI architecture and optimized model performance. Ekko provided identity verification, policy enforcement, and secure data orchestration.

“The healthcare blueprint established by this partnership applies directly to other regulated industries requiring data sovereignty. Financial services, government agencies, and global enterprises face the same challenges and need anti-fraud detection and risk analysis across distributed systems and intelligence collaboration across jurisdictions. Enterprises need operational insights across global subsidiaries. Federated AI is a fundamental architectural pattern for operating AI at scale in multi-stakeholder, regulated environments without centralizing sensitive data,” said Vince Albanese, CEO & President, Red Rock Group Holdings

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Let’s discuss how Axonis helps you operationalize AI without compromising compliance or governance.

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