EHR Integration

from rubric import Rubric
from rubric.integrations import EpicFHIR

client = Rubric()

# Configure Epic SMART on FHIR integration
epic = client.integrations.ehr.configure(
    provider="epic",
    integration_type="smart_on_fhir",

    # Epic App Orchard credentials
    client_id="your-client-id",
    client_secret="your-client-secret",  # Stored encrypted

    # Epic environment
    fhir_base_url="https://fhir.epic.com/interconnect-fhir-oauth/api/FHIR/R4",

    # Scopes requested
    scopes=[
        "patient/*.read",
        "user/*.read",
        "launch/patient"
    ],

    # Data access configuration
    data_access={
        "patient_demographics": True,
        "clinical_notes": True,
        "medications": True,
        "lab_results": True,
        "vital_signs": True,
        "diagnoses": True
    }
)

print(f"Epic integration configured: {epic.id}")

from rubric import Rubric

client = Rubric()

# Fetch patient data for evaluation
patient_data = client.integrations.ehr.fetch_patient(
    integration_id="epic_prod",
    patient_id="E12345",

    # What to fetch
    include=[
        "demographics",
        "recent_encounters",
        "active_medications",
        "recent_labs"
    ],

    # Time range
    encounter_lookback_days=90
)

# Log for evaluation with EHR context
client.calls.log(
    project="triage-ai",

    input={
        "patient_context": patient_data.to_dict(),
        "chief_complaint": "chest pain",
        "transcript": "..."
    },

    output={
        "triage_level": "urgent",
        "reasoning": "..."
    },

    # Link to EHR record
    ehr_reference={
        "system": "epic",
        "patient_id": "E12345",
        "encounter_id": "ENC-67890"
    }
)

from rubric import Rubric

client = Rubric()

# Configure Cerner SMART on FHIR
cerner = client.integrations.ehr.configure(
    provider="cerner",
    integration_type="smart_on_fhir",

    # Cerner Code Console credentials
    client_id="your-client-id",
    client_secret="your-client-secret",

    # Cerner FHIR endpoint
    fhir_base_url="https://fhir-ehr.cerner.com/r4/your-tenant-id",

    scopes=[
        "patient/Patient.read",
        "patient/Observation.read",
        "patient/MedicationRequest.read",
        "patient/DocumentReference.read"
    ]
)

from rubric import Rubric

client = Rubric()

# Configure batch extraction job
extraction_job = client.integrations.ehr.batch_extract(
    integration_id="epic_prod",

    # Extraction criteria
    criteria={
        "date_range": {
            "start": "2024-01-01",
            "end": "2024-03-31"
        },
        "encounter_types": ["emergency", "urgent_care"],
        "departments": ["ED", "Urgent Care"],
        "sample_size": 1000
    },

    # What to extract
    include=[
        "demographics",
        "chief_complaint",
        "clinical_notes",
        "triage_assessment",
        "disposition"
    ],

    # De-identification
    deidentify={
        "enabled": True,
        "method": "safe_harbor",
        "preserve_dates": "shift"  # Shift dates consistently
    },

    # Output
    output_dataset="ehr_extraction_q1_2024"
)

print(f"Extraction job started: {extraction_job.id}")
print(f"Estimated records: {extraction_job.estimated_records}")

from rubric import Rubric
from rubric.integrations import CDSHooks

client = Rubric()

# Configure CDS Hooks endpoint for evaluation
cds_config = client.integrations.cds_hooks.configure(
    # Your CDS service endpoint
    service_endpoint="https://your-ai.example.com/cds-services",

    # Hooks to capture
    hooks=[
        "patient-view",
        "order-select",
        "order-sign"
    ],

    # Capture configuration
    capture_requests=True,
    capture_responses=True,
    capture_feedback=True,  # If clinicians provide feedback

    # Route to Rubric project
    target_project="cds-ai-evaluation"
)