AI Agentic Workflow for Multi-Modal Emergency Signal Verification

Unverified emergency alerts from PERS devices or fall detectors create significant operational and financial waste. Each false alarm consumes 30-45 minutes of EMS crew time, incurs dispatch fees, and causes unnecessary patient stress. For a health system managing a remote care cohort, this can translate to hundreds of thousands in wasted resources annually and degraded response times for true emergencies. The business case centers on reducing false-positive dispatch rates by 70-90% through automated, multi-source verification.

Implementation requires a fault-tolerant orchestration layer, typically built with LangGraph or a custom state machine, to manage the verification sequence and timeouts. Data flows integrate via APIs with IoT platforms (e.g., AWS IoT Core), wearable SDKs, and clinical systems like Epic for patient context. Critical controls include configurable confidence thresholds, mandatory human-in-the-loop review for medium-risk scores, and full audit trails for compliance. The architecture must handle poor connectivity, sensor failure, and privacy-preserving video analysis at the edge to maintain responsiveness and trust.

This workflow automates the critical verification bottleneck in remote elder care, where a single sensor alert (e.g., a fall detector) is insufficient for reliable dispatch. By orchestrating specialized agents to analyze correlated data streams—ambient sound for distress cues, motion sensors for post-fall movement, and privacy-preserving camera analysis—the system confirms the incident's validity. This prevents unnecessary EMS deployments, conserving public resources and avoiding traumatic false alarms for the senior, while ensuring verified emergencies receive faster, more informed response.

Implementation integrates with existing PERS devices, smart home hubs (e.g., Samsung SmartThings), and clinical systems like Epic via HL7/FHIR. The orchestrator, built on frameworks like LangGraph, manages agent execution, confidence aggregation, and audit trails. Critical controls include configurable verification thresholds, mandatory human-in-the-loop review for low-confidence outcomes, and immutable logging for compliance. Rollout requires phased sensor integration and rigorous testing against historical false-alarm data to tune agent logic and thresholds.

This workflow automates the critical bottleneck of validating emergency alerts before dispatching costly EMS resources. It ingests an initial trigger, such as a fall detector signal, and orchestrates a verification sequence with secondary data streams—ambient sound analysis, motion sensors, and privacy-blurred camera feeds. The operational upside comes from reducing false positives by over 70%, which directly conserves emergency responder capacity, prevents unnecessary patient stress, and lowers associated fines for facilities. Implementation requires integrating with IoT platforms like PERS devices, smart home hubs, and clinical communication systems.

Phase 1 establishes the core orchestrator and integrates the primary alert source and one verification stream (e.g., sound). Phase 2 adds secondary sensors and the human review queue within a platform like ServiceNow for exception handling. The final phase implements full confidence scoring, audit trails, and direct integration with Computer-Aided Dispatch (CAD) systems. Governance controls include configurable thresholds for auto-dispatch, mandatory human review for low-confidence cases, and detailed logging for compliance with healthcare regulations like HIPAA and safety standards.

Governance begins with immutable audit trails. Every verification event—from the initial fall detector trigger to the final EMS dispatch decision—must be logged with timestamps, agent reasoning, data sources, and confidence scores. This creates a defensible record for regulatory review and incident analysis. Controls are implemented as configurable approval gates within the orchestration layer (e.g., LangGraph), allowing operators to set thresholds for automated action versus human-in-the-loop review based on signal ambiguity, patient history, or time of day.

Phased rollout mitigates operational risk. Start with a shadow mode, where the workflow processes live signals but only logs recommendations without acting. Progress to a pilot with non-critical alerts in a controlled environment, such as a single assisted living facility, using the human triage console for 100% of outputs. Finally, graduate to full automation for high-confidence events, continuously monitoring false-positive rates and response times. This staged approach de-risks integration with legacy nurse call systems and PERS devices while proving ROI through conserved EMS resources.