AI Integration Platform
A Complete Clinical AI Lifecycle Platform
Core Mobile’s provides an advanced clinical AI integration platform built to manage the entire AI model lifecycle from data selection and annotation to real-time deployment and inference, securely within the hospital’s IT environment.
PCSIP is engineered to support real-world, context-aware AI across complex care environments. It leverages Ambient AI to collect actionable clinical signals directly from provider-patient and clinician-to-clinician conversations, whether conducted in person or virtually. It supports intelligent model orchestration using real-time, workflow-specific inputs that align with department-specific protocols.
Designed to be interoperable, transparent, and extensible, PCSIP integrates clinical and research data systems using standard healthcare protocols, while ensuring patient privacy, traceability, and audit readiness at every step.
Key Benefits:
Full AI Lifecycle Management: Enables hospitals to develop, test, deploy, and monitor AI models tailored to their own patient populations.
Context-Aware Data Collection: Uses Ambient AI to gather clinically relevant inputs based on live conversations during care delivery.
Workflow-Driven Design: Maps to clinical workflows using BPMN for adaptability across departments and care types.
Real-Time & Batch Inference: Supports streaming AI applications (e.g. ICU, EM transport) and historical data analysis.
Compliance-Ready Architecture: Designed for HIPAA, FISMA, and NIST RMF 800-53 compliance for clinical and federal use.
Features:
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Ambient AI Integration
Captures clinically relevant conversation data during patient encounters or staff collaboration (in-person or via Teams/Zoom), feeding AI models with rich, context-specific inputs.
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Workflow-Specific BPMN Modeling
Uses Business Process Model and Notation (BPMN) diagrams to define and map processes across specialties, ensuring interoperability, consistency, and rapid adoption across Emergency, Surgery, Radiation Oncology, Behavioral Health, ICU, NICU, Case Management, and more.
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Contextual Data Selection
Initiates model development with hospital-approved, pseudonymized patient cohorts. Data is temporarily staged in secure, local repositories for validation and training.
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Domain Expert Annotation Tools
Enables clinical teams to annotate datasets through integrated tools, complete with feedback loops and flexible storage for versioning and reuse.
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Local Model Training & Validation
Performs AI model training on-site using pseudonymized data. Supports both technical and clinical validation to ensure accuracy, safety, and effectiveness.
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Real-Time & Batch Inference
Delivers AI model results in real time for monitoring-intensive scenarios (ICU, transport, video feeds), and in batch mode for retrospective analysis, research, and population health.
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Interoperability Across Clinical Systems
Bridges hospital and research IT environments by integrating with EHRs, PACS, EKG systems, bedside monitors, and more using HL7, FHIR, DICOM, REST, and WSDL protocols.
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Auditability & Observability
All model outputs and metadata are logged to clinical and research data marts, supporting traceability, audit trails, and regulatory compliance.
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Privacy & Security by Design
Architecture supports future extensibility into federated learning, advanced privacy modules, and full alignment with NIST RMF 800-53 and FISMA requirements.
