Automation Workflow for Cardiology Stress Test Interpretation and Reporting

This workflow automates the ingestion of raw stress test data—ECG waveforms, imaging files, and hemodynamic parameters—and applies guideline-based logic to draft a structured diagnostic report. It directly targets the 15-20 minutes of repetitive documentation per test, a bottleneck that limits cardiologist throughput. Savings come from standardizing report quality, reducing transcription costs, and freeing specialist time for higher-value clinical decisions, while the structured output ensures consistency for downstream care and billing.

Implementation integrates with hospital EHRs like Epic or Cerner via HL7/FHIR, using an orchestrator such as LangGraph to manage specialized agents for data parsing, guideline checking (e.g., ACC/AHA), and narrative generation. Controls include automated data quality checks, an explainability layer that logs the clinical rationale for each finding, and configurable approval gates. High-confidence normal reports can auto-sign, while abnormal or low-confidence findings route to a cardiologist's queue within the EHR, preserving mandatory oversight and creating a defensible audit trail for accreditation and billing audits.

This workflow automates a high-volume, repetitive bottleneck where cardiologists manually interpret ECG, imaging, and hemodynamic data against clinical guidelines to draft reports. The operational upside comes from reducing report generation from 15-20 minutes to near-instantaneous draft creation, freeing specialist time for complex cases. Savings are realized through increased physician throughput and reduced transcription costs, while standardized, guideline-adherent reporting improves quality metrics and supports value-based care contracts.

Implementation integrates directly with PACS and EHR systems like Epic or Cerner via HL7/FHIR. The orchestrator, built on LangGraph, sequences specialized validation, interpretation, and drafting agents. Critical controls include an anomaly score threshold for mandatory cardiologist review, explicit citation of guideline logic (e.g., ACC/AHA) within the report, and a full audit trail logging all agent decisions and data inputs for compliance. Rollout requires phased validation against historical reports and parallel running before full automation.

Phase 1 focuses on a co-pilot architecture where the AI drafts findings from raw ECG, imaging, and hemodynamic data, but all reports require full cardiologist review and sign-off within the existing EHR (e.g., Epic, Cerner). This initial stage delivers immediate time savings in report composition while building trust in the system's output quality and clinical reasoning traceability. The workflow integrates via HL7/FHIR APIs, with all AI-generated content and cardiologist edits logged for audit.

Phase 2 introduces conditional autonomy, allowing auto-signoff for low-risk, high-confidence interpretations while routing complex or ambiguous cases for human review. This stage requires robust confidence scoring, exception handling logic, and a dedicated oversight dashboard for the lead cardiologist. The final phase expands to full-scale deployment, integrating continuous validation against new guidelines and outcomes data. Each phase is governed by a clinical steering committee, with performance metrics on report turnaround, cardiologist time saved, and diagnostic concordance tracked against the phased business case.

Effective governance begins with a clear, auditable decision trail. The workflow must log every input, model inference, and logic step, linking final report findings to source ECG data, imaging pixels, and applied guideline rules (e.g., ACC/AHA). This explainability layer is non-negotiable for defending interpretations during peer review, audits, or potential liability inquiries. Controls include configurable confidence thresholds that automatically route low-confidence interpretations or significant abnormalities to a cardiologist for mandatory review before report finalization, ensuring human oversight is preserved for high-risk cases.

A phased rollout mitigates operational risk. Phase 1 deploys the system in 'assistive' mode, generating draft reports for all cases but requiring full cardiologist review and edit, building trust and refining logic. Phase 2 activates autonomous drafting for a subset of low-complexity, high-confidence cases based on validated performance, freeing specialist time. Phase 3 expands autonomy while integrating continuous monitoring dashboards that track report turnaround, override rates, and discrepancy analysis against a gold-standard sample, ensuring the system's performance and clinical alignment are continuously validated before full-scale deployment.

This automation directly addresses the operational bottleneck of manual report drafting, which consumes 15-25 minutes per study. By ingesting ECG waveforms, imaging data, and hemodynamic signals from EMR and PACS systems, a custom orchestration layer applies ACC/AHA guideline logic to generate preliminary findings, impressions, and recommendations. The business value is clear: a 70% reduction in cardiologist reporting time, improved report standardization, and the elimination of transcription costs, directly increasing clinic throughput and revenue per physician.

Implementation requires a LangGraph or similar orchestrator to manage the sequential and conditional logic between data validation, multimodal analysis (ECG, imaging, patient history), and report generation. Critical controls include automated data quality gates, confidence-based routing to cardiologist review, and an immutable explanation layer that logs the guideline criteria and data points behind each interpretation. This architecture, integrated with Epic or Cerner, ensures the workflow meets clinical safety standards and creates a defensible audit trail for accreditation and reimbursement audits.