Multi-Agent Based Automation of Critical Finding Escalation

Critical diagnostic findings—like a new mass suggestive of cancer or an acute stroke—require immediate, documented communication to the treating clinician. Manual phone tag and inbox notifications fail systematically, creating patient safety and legal exposure. A custom multi-agent workflow automates this high-stakes handoff. It ingests structured and unstructured data from radiologist dictation systems (e.g., Nuance PowerScribe) and PACS, classifies urgency using NLP, and orchestrates guaranteed delivery with acknowledgment tracking. The operational upside is measured in reduced time-to-treatment and eliminated communication-related sentinel events.

Implementation requires integrating with the hospital's HL7 ADT and ORU feeds to establish patient context and clinician assignment. The Classification Agent is built on a fine-tuned LLM, validated against historical reports to minimize false positives. The Orchestration Agent, built in LangGraph, manages state, enforces SLAs (e.g., 30-minute acknowledgment), and triggers failover to backup providers or supervisor pagers. Controls include a human-in-the-loop review queue for borderline-urgency cases and a dashboard for real-time monitoring of all active escalations. The architecture must be deployed within the health system's secure enclave, with all actions written to an immutable audit log for compliance.

The workflow begins when a critical finding is identified, either by a radiologist dictating into a PACS/RIS or by an AI pre-screening agent analyzing an imaging study. This event triggers a Classification Agent, which validates the finding against clinical severity rules and enriches it with patient context from the EHR via FHIR APIs. The agent then packages the finding—including priority, relevant prior studies, and patient location—into a structured alert payload for the Orchestrator.

The Orchestrator manages state, invoking a Communication Agent to dispatch the alert via the primary channel (e.g., EHR in-basket, secure chat). A state-tracking monitor initiates a configurable acknowledgment timer. If the timer expires, the Orchestrator triggers an Escalation Agent to re-route the alert via a secondary channel (e.g., pager, mobile push). A final Failover Agent engages after a second timeout, notifying a charge nurse or on-call pool. An Audit Agent logs every state transition, API call, and acknowledgment to an immutable ledger for compliance and root-cause analysis, ensuring full traceability.

This workflow automates the identification, routing, acknowledgment tracking, and failover escalation of critical results from systems like PACS and dictation software. It eliminates the manual, error-prone process of phone calls and pagers, reducing time-to-intervention from hours to minutes. The operational upside is a closed-loop communication system with guaranteed delivery, reducing legal exposure and improving outcomes for acute strokes, critical cancer diagnoses, or incidental pulmonary emboli. Savings come from preventing costly adverse events and reclaiming clinician time spent on administrative follow-up.

Implementation begins by integrating agents with the radiologist's dictation system (e.g., Nuance PowerScribe) via HL7 or API to capture preliminary reports. The Classification Agent uses NLP to identify critical keywords and confidence scores, triggering the Orchestration Agent built on LangGraph or a custom state machine. The Communication Agent interfaces with hospital paging (e.g., Spok) and EHR APIs (Epic, Cerner) for in-basket creation. Controls include configurable timeout rules, mandatory human-in-the-loop for final acknowledgment, and a full audit trail logged to a secure database for compliance reviews and root-cause analysis of any failures.

This workflow automates the high-risk, manual process of communicating critical results—like acute stroke or critical cancer diagnoses—from radiologists to treating clinicians. It eliminates phone tag, missed pages, and unverified handoffs that create patient safety incidents and legal liability. The operational upside comes from guaranteed, auditable delivery that reduces time-to-intervention and protects against never-event penalties, while freeing clinical staff from administrative tracking. Implementation integrates directly with radiologist dictation systems (e.g., Nuance PowerScribe), hospital communication protocols (e.g., HIPAA-compliant secure messaging, EHR in-baskets), and on-call schedules.

The solution architecture deploys specialized agents: a Classification Agent parses NLP output from dictation to flag criticality based on institutional rules; an Orchestrator Agent manages state, routes the alert via HL7 to the EHR or secure messaging API, and starts a configurable acknowledgment timer; an Escalation Agent executes failover logic using on-call schedules from systems like Amion. Controls are essential: all actions are logged to an immutable audit trail for compliance; human-in-the-loop verification is required before initial AI classification; and escalation paths must be validated with clinical leadership. Phased rollout starts with non-life-threatening findings in a single department before expanding to all critical results.