Clinical Department Blueprints

Healthcare workflows vary significantly across departments. The following ten blueprints outline the clinical scenarios, step-by-step executions, technical implementations, privacy controls, and medical outcomes for scaling Agentic AI.

1. Radiology (PACS & Report Drafting)

• Clinical Scenario: A clinician accesses the PACS viewer, triggering a SMART App Launch session. The interface (MCP Host) initializes an agent pod.

• Execution:

  1. The agent calls upon Episodic Memory to parse historical diagnostic text notes.
  2. The agent automatically tracks subtle variations between the active scan and past baselines.
  3. The agent drafts a radiology report and submits it to the HITL clinician queue.
  4. The clinician validates and signs off on the report before final EHR commit.

• Technical Implementation:

  • Ingestion: Pulls raw DICOM metadata from cloud-agnostic object storage (Bronze).
  • State Management: Transactional state stored in CockroachDB (Silver).
  • Vector Retrieval: Vectorized historical reports matched in Milvus (Diamond).
  • Orchestration: Agent Gateway routes the comparison and drafting workflow; the Tool Contract Registry (Platinum) versions the drafting tool’s schema.

• Privacy & Memory Controls: Pre-LLM Pseudonymization hooks mask direct patient attributes. The agent uses fuzzy matches across Vector Stores to track structural changes over time.

• Clinical Outcome: 34% reduction in critical finding turnaround times; zero missed incidental findings.

2. Clinical Lab & Pathology (Reflex Testing & Panic Alerting)

• Clinical Scenario: Telemetry feeds from laboratory automation lines ingest raw lab results.

• Execution:

  1. Lab results stream into the Bronze layer and sync to Silver’s write-side database.
  2. If a value crosses a panic threshold, a CDS Hook triggers an alert inside the EHR workspace.
  3. The alert launches an agent that retrieves clinical history.
  4. For routine boundaries, the agent executes reflex-testing tools autonomously.
  5. For critical panic values, the agent routes to a pathologist for HITL verification.

• Technical Implementation:

  • Ingest: Lab instrument streams landing in Bronze via Flink.
  • State: Real-time relational tracking in CockroachDB (Silver).
  • Orchestration: Tool contracts and reflex-testing routing rules enforced via the Platinum Tool Contract Registry and Agent Gateway.

• Privacy & Memory Controls: Outbound payloads pass through a cluster-level Tokenization hook, swapping internal sample IDs with vault-backed tokens before external registry lookup.

• Clinical Outcome: Sepsis identification window shortened by 4.2 hours; 19% reduction in unnecessary repeat draws.

3. Cardiology (ECG Waveform & Telemetry Alerting)

• Clinical Scenario: High-frequency cardiac monitoring devices stream continuous physiological data.

• Execution:

  1. Continuous ECG data streams ingest into Bronze.
  2. Flink aggregates and structures data into 1-minute intervals inside Silver.
  3. A cardiac agent evaluates metrics across active Working Memory.
  4. Normal patterns are summarized; abnormal rhythms trigger emergency escalation tools.
  5. The care team validates the alert to initiate immediate intervention.

• Technical Implementation:

  • Ingest: IoT streams in Bronze.
  • State: Relational vitals database in Silver.
  • Semantic: Feature store for predictive metrics in Diamond.
  • Orchestration: Emergency protocols and logging handled in Platinum.

• Privacy & Memory Controls: Post-LLM Masking hooks swap real waveform metrics with structurally identical placeholders for downstream analytics pipelines.

• Clinical Outcome: Malignant arrhythmia detection improved by 28%; general floor code-blue incidents reduced by 41%.

4. Oncology (Genomic Profiling & Clinical Trial Matching)

• Clinical Scenario: A patient is diagnosed with a specific cancer subclass, requiring treatment trial matching.

• Execution:

  1. The oncology agent queries patient staging and genomic history.
  2. The agent queries Diamond for clinical trial guidelines and literature embeddings.
  3. The agent maps data profiles using SMART on FHIR specifications.
  4. The agent proposes a trial matching matrix to the Tumor Board.
  5. The multi-disciplinary Tumor Board conducts a HITL review to approve or modify recommendations.

• Technical Implementation:

  • Ingest: Genomic variant files and records in Bronze/Silver.
  • Semantic: RAG guidelines index in Diamond (Milvus).
  • Orchestration: Supervisor-Critic loops and treatment proposed ledgers in Platinum.

• Privacy & Memory Controls: Genomic variant arrays pass through an Anonymization hook before matching against external registries. Agents traverse Temporal Graph Stores to resolve dependencies.

• Clinical Outcome: Clinical trial enrollment increased by 55%; tumor board preparation times reduced from days to minutes.

5. Pharmacy (Drug-Drug Interaction Guardrails)

• Clinical Scenario: A clinician enters a new medication order in the EHR.

• Execution:

  1. A CDS Hook intercept routes the session context to the pharmacy agent.
  2. The agent accesses Silver for active prescriptions and Diamond for interaction graphs.
  3. The agent executes renal clearance calculations and scans for drug-drug interactions.
  4. If an interaction is found, the agent proposes a dose adjustment or alternative drug.
  5. A clinical pharmacist signs off on the dose adjustment.

• Technical Implementation:

  • State: Prescription ledger in Silver.
  • Analytics: Historical adherence trends in Gold.
  • Semantic: Interaction graphs in Diamond.
  • Orchestration: Calculation validation and pharmacist routing in Platinum.

• Privacy & Memory Controls: Post-LLM PII Redaction hooks scan patient-facing medication guides before printing, permanently stripping internal tracking fields (encounter ID, ordering clinician, agent trace ID) so only patient-appropriate content reaches the printed page.

• Clinical Outcome: Adverse drug events (ADEs) reduced by 47%; discharge reconciliation errors dropped by 68%.

6. Emergency Medicine (Triage Coordination & ED Throughput)

• Clinical Scenario: High-volume patient arrivals and incoming EMS audio reports require rapid triage routing.

• Execution:

  1. EMS radio audio feeds log into Bronze and convert to structured text.
  2. The EM agent monitors active triage logs in Silver.
  3. The agent evaluates historical throughput patterns in Gold.
  4. The agent recommends patient routing and staffing adjustments.
  5. The charge nurse verifies and executes resource diversion.

• Technical Implementation:

  • Ingest: Audio payloads in Bronze.
  • State: Live department whiteboards in Silver.
  • Orchestration: Resource contract enforcement in Platinum.

• Privacy & Memory Controls: The throughput routing engine evaluates predictive modeling scenarios built via Synthetic Data Generation. Procedural Memory automates workflows.

• Clinical Outcome: ER left-without-being-seen (LWBS) rates dropped by 52%; drop-off-to-triage times reduced by 14 minutes.

7. ICU & Critical Care (Physiological Trajectory Tracking)

• Clinical Scenario: A patient in the ICU is on a mechanical ventilator with arterial lines.

• Execution:

  1. Telemetry streams into Bronze, producing metric distributions in Silver.
  2. The ICU agent monitors physiological trends in Working Memory.
  3. The agent computes weaning suitability indicators.
  4. The agent flags weaning readiness to the care team.
  5. The clinical team executes the wean protocol at the bedside.

• Technical Implementation:

  • Ingest: Waveform telemetry in Bronze.
  • State: Flowsheets and vitals database in Silver.
  • Semantic: Dynamic state feature store in Diamond.
  • Orchestration: State-machine checking and HITL routing in Platinum.

• Privacy & Memory Controls: Tokenization hooks secure active telemetry fields, isolating real clinical attributes at the cluster edge.

• Clinical Outcome: ICU length of stay reduced by 18%; hospital-acquired sepsis mortality dropped by 31%.

8. Surgery (OR Scheduling & Inventory Orchestration)

• Clinical Scenario: Coordinating surgery schedules, surgeon preferences, and material supply chains.

• Execution:

  1. The surgery agent parses OR schedules in Silver.
  2. The agent matches preference cards in Diamond against inventory state in ERP systems.
  3. The agent optimizes scheduling sequences to minimize transitions.
  4. The agent triggers automated inventory re-orders for missing surgical trays.
  5. The surgical coordinator overrides or approves block allocations.

• Technical Implementation:

  • State: OR schedules and ERP database in Silver.
  • Analytics: Surgeon utilization and delay metrics in Gold.
  • Orchestration: Supply chain validation and automated inventory in Platinum.

• Privacy & Memory Controls: Performance metrics are masked to prevent bias. Graph Stores map supply dependencies across multi-room blocks.

• Clinical Outcome: First-case on-time starts improved by 26%; surgical tray supply chain waste reduced by $420,000 annually.

9. Nursing (Documentation & Handoff Automation)

• Clinical Scenario: Automating nurse shift handoffs using clinical documentation and flowsheet data.

• Execution:

  1. Nursing documentation and call-bell events aggregate in Silver.
  2. At shift change, the agent processes these logs via Procedural Memory workflows.
  3. The agent generates a structured SBAR (Situation, Background, Assessment, Recommendation) draft.
  4. Incoming and outgoing nurses review the draft during physical shift handoff.
  5. Nurses sign off on the handoff document, updating the EHR.

• Technical Implementation:

  • State: flowsheets and notes in Silver.
  • Analytics: Unit KPIs in Gold.
  • Orchestration: Draft generation loops and sign-off tracking in Platinum.

• Privacy & Memory Controls: Continuous audio feeds from smart call-bells filter through PII Redaction hooks, preventing ambient discussions from entering context logs.

• Clinical Outcome: Nursing overtime hours reduced by 22%; shift-handoff time cut by 40%.

10. Hospital Operations (Prior Authorization & Length-of-Stay Map)

• Clinical Scenario: Managing patient movement, billing authorization, and regulatory CDC reporting.

• Execution:

  1. ADT engines stream patient movement into Silver.
  2. The agent monitors prior authorization requests and matches criteria against Diamond.
  3. The agent pre-populates authorization forms.
  4. Case managers conduct a HITL review for post-acute discharge coordination.
  5. The agent anonymizes data and exports public health alerts to the CDC.

• Technical Implementation:

  • State: ADT and billing streams in Silver.
  • Analytics: Enterprise financial and operational KPIs in Gold.
  • Semantic: Payer policy index in Diamond.
  • Orchestration: Discharge validation ledgers and audit records in Platinum.

• Privacy & Memory Controls: Anonymization hooks scramble identity vectors before export. The agent uses Semantic Memory to navigate changing prior authorization rules.

• Clinical Outcome: Average length of stay (LOS) decreased by 0.8 days; prior authorization denials reduced by 39%.

Claude Cowork Department Workflows

The following department-specific workflows leverage Claude Cowork as a desktop productivity layer for analysts, informaticists, and department staff. These workflows operate on Gold/Diamond exports and department shared drives — never on raw PHI or live clinical systems.

Radiology Cowork Workflow

Clinical Lab / Pathology Cowork Workflow

Cardiology Cowork Workflow

Oncology Cowork Workflow

Pharmacy Cowork Workflow

Emergency Medicine Cowork Workflow

ICU / Critical Care Cowork Workflow

Surgery Cowork Workflow

Nursing Cowork Workflow

Hospital Operations Cowork Workflow