Multilingual Report Translation and Patient Summaries Automation

This workflow automates the high-cost, high-latency bottleneck of translating complex radiology reports and creating accessible patient summaries. It directly addresses health equity and operational efficiency by eliminating dependency on manual translation services, which are expensive and slow. The architecture ingests finalized reports from the RIS/PACS, uses specialized clinical LLM agents to preserve medical terminology accuracy across languages, and routes outputs through mandatory quality assurance gates. Savings come from reduced interpreter fees, decreased administrative follow-up calls, and improved patient comprehension leading to better adherence and fewer missed appointments.

Implementation requires a LangGraph-based orchestrator to manage state, handle retries, and enforce audit trails. Clinical LLM agents must be fine-tuned or prompted with medical ontologies and target-language glossaries to ensure diagnostic precision is not lost. The workflow integrates with Epic or Cerner EHRs via FHIR APIs for secure delivery. Critical controls include a human-in-the-loop review for all initial translations per language-model pair, automated confidence scoring to flag low-certainty segments, and comprehensive logging of all overrides for model improvement and regulatory compliance. Rollout is sequenced by language and report type to manage risk.

This workflow eliminates the manual bottleneck of report translation and patient communication, which delays care and inflates interpreter costs. It ingests finalized DICOM Structured Reports or HL7 messages from the PACS/RIS, triggering a clinical LLM agent specialized in medical terminology. The agent first translates the technical impression into the target language, preserving diagnostic accuracy, then generates a plain-language summary suitable for patient education. This dual-step process ensures clinical fidelity while improving comprehension, directly reducing reliance on third-party translation services and administrative follow-up.

Implementation requires tight integration with the EHR (e.g., Epic, Cerner) for patient language preference and secure delivery via patient portals. The orchestrator, built on LangGraph, manages state, handles exceptions, and enforces approval gates based on confidence scores from the translation agent. A mandatory human-in-the-loop review is triggered for low-confidence outputs or specific high-risk findings. This architecture creates a defensible, auditable pipeline that reduces per-report translation costs by over 60% while standardizing patient communication across diverse populations.

This workflow automates the translation of complex radiology reports and the creation of accessible patient summaries, directly addressing the operational bottleneck of manual translation and patient communication. Savings come from reducing reliance on expensive human interpreters by 40-60% and cutting clinician time spent explaining findings. The architecture integrates with the PACS/RIS via HL7/FHIR, triggers specialized clinical LLM agents for domain-accurate translation, and routes outputs through a mandatory quality assurance gate before secure delivery to patient portals or print systems.

Implementation requires deploying the orchestration layer (e.g., LangGraph) within the hospital's secure cloud, integrating with the EHR for patient language preferences, and establishing a review queue in the existing clinical workflow system (e.g., Epic, Cerner). Key controls include confidence scoring on translations, logging all overrides for model retraining, and maintaining a full audit trail for compliance. Rollout should be sequenced by department, starting with high-volume, lower-acuity modalities like screening mammography to validate safety and throughput gains before expanding.

The rollout begins with a pilot on non-critical, finalized reports from a single modality (e.g., screening mammography). This isolates risk while validating the translation agent's handling of domain-specific terminology against a certified human translator. Integration is staged: first, a batch export from the PACS/RIS to a secure processing environment; later, a real-time HL7/FHIR listener. Each phase requires sign-off from Radiology, Compliance, and IT Security, with clear rollback procedures defined. The goal is to demonstrate a 40-60% reduction in interpreter service costs and improved patient comprehension scores before expanding.

Post-pilot, expansion is governed by a clinical steering committee. The workflow scales by adding modalities (CT, MRI) and languages, each requiring updated terminology validation. A continuous monitoring layer tracks translation confidence scores, physician override rates, and patient feedback, triggering model retraining if drift is detected. The final architecture embeds the orchestrator within the hospital's integration engine (e.g., Mirth Connect), with all outputs logged to the EHR for a complete audit trail. This controlled, metrics-driven approach ensures the automation improves health equity without introducing clinical risk or compliance gaps.