AI-Powered Workflow for Oncology Tumor Board Data Preparation

Manual assembly of imaging, pathology, genomics, and prior treatment data from disparate systems (PACS, LIS, EHR modules like Epic Beaker/Oncology) typically consumes 45–90 minutes of coordinator or clinician time per case. A custom agentic workflow automates queries, retrieval, and summarization, collapsing this to under 10 minutes of automated runtime, freeing clinical staff for patient-facing or analytical work.

By eliminating the preparation bottleneck, the same clinical team can review more patients per session or dedicate saved time to deeper discussion of complex cases. This directly accelerates time-to-treatment decisions for cancer patients and improves utilization of scarce multidisciplinary specialist time.

The workflow automates the repetitive, error-prone task of logging into 5+ systems (EHR, PACS, molecular diagnostics platforms like Foundation Medicine), downloading reports, and copying key findings into a briefing packet. This removes a major source of coordinator burnout and reduces the risk of missing critical data points (e.g., a recent biomarker result) that could alter treatment recommendations.

Manual preparation leads to inconsistent formatting and variable data inclusion. The automated workflow enforces a standardized template aligned with NCCN guidelines, ensuring every packet contains all required data elements (imaging timelines, pathology summaries, genomic variants, performance status). This creates a defensible audit trail for accreditation and improves the consistency and quality of board deliberations.

Clinicians arrive at the tumor board with a pre-synthesized narrative—agents summarize key trends in lab values, highlight discrepancies between radiology and pathology reports, and flag actionable mutations. This reduces the pre-meeting mental assembly required from 15–20 minutes of skimming dense reports to under 2 minutes of reviewing a structured summary, preserving cognitive energy for complex decision-making.

Manual interpretation and formatting of next-generation sequencing (NGS) reports is a specialist task that delays packet readiness. The workflow uses NLP agents fine-tuned for genomic reports to extract variants, interpret clinical significance (Tier I/II), and match them to trial eligibility in seconds. This brings precision medicine data into the board discussion faster, potentially unlocking targeted therapy options that manual processes might miss or delay.

This primary agent acts as the workflow conductor. It receives the tumor board schedule from the oncology EHR module (e.g., Epic Beacon, Cerner Oncology), resolves the patient identity, and triggers parallel data-fetching tasks. It manages API calls and HL7/FHIR queries to PACS for imaging, the Laboratory Information System (LIS) for labs, the molecular diagnostics platform for genomics, and the pharmacy system for treatment history. It handles retries, timeouts, and logs all data provenance for audit.

For each data modality, a specialized agent extracts and summarizes key findings into a clinician-ready format. The Imaging Agent pulls relevant DICOM series and runs a pre-screening model to highlight suspicious lesions, appending measurements and prior comparisons. The Pathology Agent parses structured synoptic reports and free-text notes to summarize tumor grade, stage, and biomarker status (e.g., PD-L1, HER2). The Genomics Agent interprets variant call format (VCF) files or PDF reports, highlighting actionable mutations and potential clinical trial matches from a curated knowledge graph.

This agent receives the summarized outputs and assembles them into a standardized, timeline-based briefing packet. It follows an institutional template, ensuring consistent sections for History of Present Illness, Prior Treatments, Imaging Findings, Pathology, Genomic Profile, Performance Status (e.g., ECOG score), and Key Questions for the Board. The agent uses a layout engine (like PDFKit or a headless Chrome instance) to generate a final PDF or HTML document, embedding hyperlinks to the original source data in the respective clinical systems for deep-dive review.

Before distribution, the assembled packet is routed through a configurable approval gate. For high-confidence, routine cases, it may auto-approve. For complex cases or where data is missing/conflicting, the workflow automatically creates a task in the assigned oncologist's EHR in-basket or a dedicated review queue in a platform like ServiceNow. The human reviewer can annotate, request re-extraction, or approve for distribution. This control loop is critical for safety and clinician trust, ensuring automation supports rather than replaces expert oversight.

The underlying platform is not a monolithic application but a custom orchestration layer built with frameworks like LangGraph or Prefect. It sits behind a secure API gateway that handles authentication (OAuth2, SMART on FHIR) and translation between disparate hospital system protocols (HL7v2, FHIR, DICOMweb, proprietary REST APIs). A centralized data cache (e.g., Redis) stores intermediate results to avoid re-querying source systems during the assembly phase, improving performance and reducing load on clinical databases.

A non-negotiable component for production deployment. This system provides real-time visibility into workflow execution: packet completion rates, data source latency, agent error rates, and exception volumes. It integrates with logging (OpenTelemetry) and monitoring (Datadog, Grafana) stacks. The dashboard shows business metrics like preparation hours saved per week and average time-to-ready packet, directly linking the technical build to the operational ROI for hospital leadership and IT governance.