Automation Workflow for Explainable Diagnostic Triage and Prioritization

This workflow automates the critical bottleneck of manually sorting and prioritizing complex diagnostic cases. By orchestrating specialist LLM agents to analyze fused EHR, imaging, and lab data, it generates risk-stratified diagnostic hypotheses with clear, cited reasoning. The operational upside is measured in reduced time-to-intervention for high-risk patients, increased clinician throughput, and mitigated diagnostic error from cognitive overload. Implementation requires a production-grade orchestration layer, typically built with LangGraph or similar frameworks, to manage data retrieval, agentic reasoning, and integration with clinical systems like Epic or Cerner.

The architecture's credibility hinges on its explainability layer and human-in-the-loop controls. Each triage decision must be accompanied by the agent's reasoning trail, citing source data, to build clinician trust and support auditability. Implementation integrates this output into a clinician-facing dashboard within the EHR or a separate portal, showing the prioritized queue and the 'why' behind each case. Practical constraints include managing data quality variances, designing exception routing for ambiguous cases, and sequencing a phased rollout—starting with non-critical support functions—to validate safety and efficacy before full clinical deployment.

This architecture automates the bottleneck of manually sorting and prioritizing incoming diagnostic cases. By fusing imaging, labs, and notes, it generates risk-stratified hypotheses with clear reasoning, directing clinician attention to high-risk patients first. The operational upside comes from reduced time-to-intervention for critical findings and improved radiologist or specialist throughput, directly impacting patient outcomes and departmental capacity. Savings are realized by shifting expensive expert labor from administrative triage to high-value diagnostic work.

Implementation centers on a stateful orchestrator, built with frameworks like LangGraph, that manages agent workflows, data handoffs, and exception routing. It integrates directly with clinical systems (Epic, Cerner, PACS) via HL7/FHIR and DICOMweb APIs. Controls are critical: each triage decision includes an explainability layer showing agent reasoning, and low-confidence or edge cases are routed to a human review dashboard. Deployment requires phased validation, starting with non-critical studies, and incorporates continuous monitoring for model drift and clinical feedback loops.

This workflow automates the initial sorting of complex diagnostic cases by orchestrating specialist LLM agents to analyze fused imaging, labs, and longitudinal records. It generates risk-stratified hypotheses with clear reasoning, eliminating manual chart review bottlenecks. The operational upside comes from reducing time-to-intervention for high-risk patients, improving clinician throughput, and lowering diagnostic error rates through systematic, context-aware analysis. Implementation begins with integrating Epic or Cerner EHRs via FHIR APIs and a PACS connector to establish a unified data ingestion layer, which feeds a central orchestration engine built on LangGraph.

Phase one delivers core ingestion and a single-agent proof-of-concept within 8 weeks, focusing on lab value triage. Phase two, at 16 weeks, integrates multimodal agents and the LangGraph orchestrator with approval gates and an explainable UI built in React, showing the 'why' behind each triage decision. The final phase, at 24 weeks, adds real-time HL7 alerting, advanced confidence scoring, and integration into clinician workflows like Epic's In-Basket. Controls include mandatory human review for low-confidence scores, audit trails for all agent reasoning, and a rollback protocol to ensure clinical safety is never compromised during incremental rollout.

The governance layer is a non-negotiable component, enforcing that no automated triage decision triggers a clinical action without passing through a configurable approval gate. This is implemented as a rules engine evaluating agent confidence scores against institutional risk thresholds—for example, routing only high-confidence, low-risk prioritizations directly to the EHR worklist while holding complex, multi-hypothesis cases for physician review. This control directly mitigates patient safety risk and builds clinician trust in the system by preserving ultimate decision authority.

Phased rollout is executed by progressively increasing the complexity of cases the system handles, beginning with a single service line (e.g., outpatient chest X-rays) before expanding to cross-modality workflows. Implementation uses feature flags in the orchestration layer (e.g., LangGraph) to control agent activation. Concurrently, a dedicated monitoring dashboard tracks key metrics—escalation rate, time-to-review, clinician override rationale—feeding a continuous governance loop. This measured approach de-risks adoption, validates ROI incrementally, and ensures the architecture scales without compromising safety or clinician workflow.

This workflow automates the initial, high-cognitive-load task of sorting incoming complex cases by clinical urgency. It ingests and fuses multimodal patient data—imaging from PACS, labs via HL7, longitudinal EHR records from Epic or Cerner, and unstructured physician notes—to generate a risk-stratified, explainable priority list. The operational upside is measured in reduced time-to-intervention for critical findings, improved radiologist and specialist throughput, and a defensible audit trail for triage decisions that supports clinical governance and mitigates diagnostic oversight risk.

Implementation centers on a LangGraph or custom orchestration layer that coordinates specialist agents—each fine-tuned or prompted for labs, imaging, and clinical rules—against a unified patient context. The architecture must integrate with hospital EHR and PACS APIs, embedding a clinician-facing UI that shows the 'why' behind each triage score via citation of source data and agent reasoning. Critical controls include configurable confidence thresholds for automated routing, mandatory human review gates for medium-risk cases, and continuous monitoring of triage accuracy against ground-truth outcomes to retrain agents and adjust logic.