Prior Exam Retrieval and Comparison Automation Workflow

This workflow automates the critical but time-consuming task of retrieving and comparing prior imaging studies, a bottleneck that consumes 20-30% of a radiologist's reading time. By orchestrating patient matching across disparate archives, performing temporal alignment, and running change-detection algorithms, it delivers a consolidated, annotated comparison view directly into the PACS worklist. The operational upside comes from eliminating manual search and mental registration, reducing cognitive load, and standardizing interval change reporting, which directly improves diagnostic throughput and longitudinal tracking accuracy for oncology, neurology, and chronic disease management.

Implementation requires a multi-agent architecture integrating with the hospital's patient master index (e.g., Epic, Cerner), PACS/VNA (e.g., Sectra, GE), and potentially cloud data lakes. The orchestrator manages DICOM retrieval, while specialized agents handle non-rigid registration and AI-powered change detection. Critical controls include confidence scoring to route uncertain alignments for human review, audit logging of all prior selections, and integration with the radiologist's reporting workflow to capture overrides. This creates a defensible, high-throughput system that scales across subspecialties without compromising safety.

This workflow directly targets the 15-20 minute bottleneck radiologists face manually searching PACS, VNAs, and data lakes for relevant priors. A custom orchestration layer, built with LangGraph or similar, deploys specialized agents for patient matching, DICOM querying, and temporal registration. The operational upside comes from eliminating this repetitive lookup and alignment work, which improves diagnostic throughput and reduces the risk of missing subtle interval changes critical for oncology or neurology tracking.

Implementation requires deep integration with clinical systems like Epic, Cerner, or Siemens PACS via HL7/FHIR and DICOMweb. The change-detection logic, often a ensemble of classical and deep learning models, runs in a containerized inference service. Critical controls include confidence scoring to route uncertain alignments for human review, audit logging of all retrievals and overrides for governance, and real-time monitoring of agent performance and data-lake query latency to ensure clinical SLA adherence.

The operational bottleneck is the manual, time-consuming search across disparate archives (PACS, VNAs, data lakes) to locate relevant prior exams, followed by visual comparison to detect subtle changes. A custom automation workflow eliminates this repetitive work by orchestrating patient matching via MPI, querying federated data sources, and deploying change-detection algorithms. The savings come from direct radiologist time reallocation and the mitigation of missed interval changes that could delay diagnosis, directly impacting patient safety and departmental throughput.

Implementation follows a phased, risk-managed approach. Phase 1 builds the core retrieval and fusion engine, integrating with the MPI and a primary PACS using HL7/FHIR. Phase 2 introduces the change-detection agent, initially as a silent second reader with exhaustive physician override logging to build a validation corpus. Phase 3 integrates the full output into the radiologist worklist (e.g., Nuance PowerScribe, Epic Radiant) with explicit confidence scoring and mandatory review for low-confidence intervals. Governance requires continuous monitoring of retrieval accuracy, change-detection performance against a gold-standard panel, and audit trails for all overrides to ensure clinical safety and support model retraining.

This workflow automates the high-friction, manual process of searching PACS/VNA archives, retrieving relevant priors, and performing temporal alignment. It eliminates 5-10 minutes of repetitive lookup and visual comparison per case, directly boosting radiologist throughput. The architecture hinges on a patient-matching orchestrator that queries data lakes using MRNs and study dates, then triggers specialized agents for DICOM normalization and 3D registration before a change-detection algorithm highlights volumetric differences, all within a governed clinical environment.

Governance is enforced via confidence scoring and mandatory review gates for low-confidence matches or significant interval changes, ensuring safety. Phased adoption begins with a shadow mode, comparing AI outputs to manual reads, then progresses to a assistive role in non-critical domains like routine follow-ups. Implementation integrates with Epic, Cerner, or existing PACS via HL7/FHIR, requiring robust logging of all retrievals, alignments, and overrides for auditability and continuous model validation against clinical ground truth.