Automation Workflow for Cardiology Stress Test and Echo Data Fusion

This specialized agent ingests raw, structured outputs from treadmill ECG monitors and blood pressure cuffs (e.g., GE CASE, Philips StressVue). It parses proprietary formats, extracts key metrics (ST-segment depression, peak heart rate, blood pressure response, exercise duration), and normalizes them into a vendor-agnostic JSON schema. This eliminates the manual transcription and data entry bottleneck that delays report initiation.

An NLP agent processes the unstructured text and DICOM SR (Structured Report) data from the echocardiogram PACS. It extracts quantitative measurements (LVEF, E/e', wall motion scores) and qualitative findings from the sonographer's or cardiologist's notes. The agent uses a fine-tuned clinical LLM to resolve ambiguities and map findings to a standardized ontology (e.g., EchoNet), ensuring data consistency for fusion.

The core orchestrator (built with LangGraph or a custom state machine) receives normalized data from both source agents. It temporally aligns stress and echo events, identifies correlations (e.g., ischemia with corresponding wall motion abnormality), and flags discrepancies for human review. This layer creates a single, coherent patient-state object that serves as the source of truth for narrative generation, replacing the fragmented view across multiple systems.

A report-specialized LLM agent uses the fused data object to generate a comprehensive, draft report in the institution's preferred format (e.g., ACC/AHA guidelines). It synthesizes stress test results with echo findings into a cohesive narrative, highlighting concordant and discordant data. The agent inserts quantitative data into templated sentences and drafts the 'Impression' and 'Recommendations' sections, providing a high-quality first draft for the cardiologist.

The drafted report is routed into a human-in-the-loop queue within the cardiologist's existing EHR (Epic, Cerner) or reporting workstation. The interface displays the AI draft side-by-side with source data for verification. The cardiologist can edit, approve, or reject sections. All changes are tracked, and final sign-off triggers automatic distribution to the patient chart and relevant referring physicians, closing the loop with a full audit trail.

Deployment involves containerized agents (Docker/Kubernetes) communicating via a message broker (Redis, RabbitMQ). Integration points are critical: HL7/FHIR APIs for EHR data (patient context, final report storage), DICOM Web for PACS connectivity, and potentially MQTT for real-time device data. A monitoring layer tracks agent performance, data quality metrics, and report turnaround time, ensuring the system meets clinical SLAs and can be iteratively improved.

Stress test systems (e.g., GE CASE, Philips StressVue) and echo machines (e.g., Philips EPIQ, GE Vivid) export data in proprietary, often non-standard formats (XML, CSV, DICOM Structured Reports). The workflow must include dedicated ingestion agents for each major vendor to parse ECG waveforms, hemodynamic measurements, and echo quantification data into a unified FHIR or internal schema. This layer handles missing fields, unit conversion, and timestamp alignment, which is critical for data consistency.

The fused report must be written back to the EHR (Epic, Cerner) as a clinical note and linked to the original imaging studies in PACS. This requires a bidirectional integration layer:

• Ingestion: Subscribe to ADT (Admission/Discharge/Transfer) and ORU (Observation Result) HL7 feeds to get patient context and trigger the workflow.
• EHR Write-back: Use FHIR API (DocumentReference, Composition) to post the draft report to the cardiologist's in-basket for review and sign-off.
• PACS Linkage: Use DICOMweb WADO-RS to retrieve echo images and ensure the final report includes direct links to the source studies within the radiologist's viewer.

The core automation logic uses specialized, sequential agents orchestrated via a framework like LangGraph or a custom state machine:

1. Data Fusion Agent: Correlates stress test measurements (peak HR, ST depression, BP response) with echo parameters (LVEF, wall motion, valve gradients) by time and procedure context.
2. Narrative Drafting Agent: An LLM fine-tuned on cardiology reports uses the fused data to generate a structured draft (Indication, Findings, Impression) with embedded measurements. It operates under strict prompting to avoid hallucination.
3. Discrepancy Flagging Agent: Cross-references the AI draft against original report text and predefined clinical logic (e.g., LVEF <50% with normal stress test) to flag inconsistencies for human review.

Safety and regulatory compliance mandate a non-negotiable review gate. The architecture must include:

• Integrated Review Queue: A UI embedded in the EHR or a separate dashboard where cardiologists can review, edit, and sign the AI-drafted report. Changes are tracked.
• Exception Routing: Cases with flagged discrepancies or low-confidence scores are prioritized in the queue or routed to a senior reviewer.
• Immutable Audit Log: Every action—data ingestion, agent decision, draft generation, physician edit, and final signature—is logged with user, timestamp, and data snapshot for full traceability, supporting MIPS/regulatory audits.

To ensure semantic interoperability, all extracted measurements and findings must be mapped to standard clinical terminologies before synthesis and storage. This dependency involves:

• LOINC: For lab and measurement codes (e.g., LP71571-9 for Left Ventricular Ejection Fraction).
• SNOMED CT: For clinical findings (e.g., 56265001 for Heart wall motion abnormality).
• UCUM: For units of measure. The workflow needs a managed terminology service (integrated or standalone) to perform this mapping automatically, ensuring the final report is structured for analytics and decision support.

A typical cardiology service may generate 50+ combined studies daily. The workflow architecture must be designed for low latency and high throughput:

• Async Processing: Use a message queue (e.g., RabbitMQ, AWS SQS) to decouple ingestion from the heavier NLP/LLM synthesis steps.
• Caching Strategy: Cache normalized patient demographics and prior reports to avoid redundant EHR queries for each run.
• GPU Management: LLM inference for report drafting must be optimized (model quantization, batch inference) to keep per-report generation under 30-60 seconds, avoiding clinician workflow disruption. Rollout typically starts with a pilot in one cath lab, scaling after validating performance and clinical acceptance.