Practical Examples - Omniloy Documentation

This page contains complete API usage examples for different clinical scenarios with the Codify ICD-10 medical coding API.

Example 1: Simple diabetes case

Use case

Process a clinical note about a diabetes patient and extract ICD-10 codes with confidence scores.

Input text

Patient presents with Type 2 Diabetes Mellitus. Currently on Metformin 500mg BID and Glipizide 5mg QD. Blood glucose levels stable at 120-140 mg/dL fasting. No complications noted. Patient reports good compliance with medications and diet. HbA1c: 6.8%. No diabetic retinopathy, nephropathy, or neuropathy.

Complete request

import requests

url = "https://{your-endpoint}/v1/codify"
headers = {
    "Authorization": "Bearer YOUR_TOKEN",
    "Content-Type": "application/json",
    "x-doctor": "dr_123",
    "x-patient": "pt_456"
}

payload = {
    "medical_note": "Patient presents with Type 2 Diabetes Mellitus. Currently on Metformin 500mg BID and Glipizide 5mg QD. Blood glucose levels stable at 120-140 mg/dL fasting. No complications noted. Patient reports good compliance with medications and diet. HbA1c: 6.8%. No diabetic retinopathy, nephropathy, or neuropathy.",
    "model": "balanced"
}

response = requests.post(url, json=payload, headers=headers)
result = response.json()

print(f"Final codes: {len(result['final_code_assessments'])}")
for assessment in result['final_code_assessments']:
    print(f"  {assessment['code']}: {assessment['description']}")
    print(f"    Confidence: {assessment['confidence_percent']}%")
    print(f"    Justification: {assessment['justification']}\n")

print(f"Run ID: {result['run_id']}")

Response

{
  "final_code_assessments": [
    {
      "code": "E11.9",
      "description": "Type 2 diabetes mellitus without complications",
      "justification": "The note documents Type 2 Diabetes without mention of decompensation or complications; corresponds to E11.9 (DM2 without complications). The stable blood glucose levels and HbA1c of 6.8% indicate controlled diabetes with no evidence of complications.",
      "confidence_percent": 95.2
    },
    {
      "code": "Z79.84",
      "description": "Long term (current) use of oral hypoglycemic drugs",
      "justification": "The patient uses metformin and glipizide chronically; both are oral antidiabetic drugs, supporting long term use of oral hypoglycemic drugs.",
      "confidence_percent": 98.1
    }
  ],
  "discarded_code_assessments": [
    {
      "code": "E11.65",
      "description": "Type 2 diabetes mellitus with hyperglycemia",
      "justification": "Discarded because blood glucose levels are stable and controlled (120-140 mg/dL fasting), not indicating hyperglycemia.",
      "confidence_percent": 12.3
    }
  ],
  "run_id": "1ef8e0d4-7890-6b3c-8f90-abcdef123456"
}

Interpretation:

E11.9: Type 2 diabetes mellitus without complications (95.2% confidence)
Z79.84: Long term use of oral hypoglycemic drugs (98.1% confidence)
The AI correctly identified controlled diabetes and discarded hyperglycemia code due to stable glucose levels

Example 2: Emergency pneumonia case

Use case

Process an emergency department note for a patient with pneumonia and extract ICD-10 codes.

Input text

45-year-old male patient presents to emergency department with fever up to 39°C, productive cough, and shortness of breath for 3 days. Physical examination reveals decreased breath sounds and crackles in right lower lobe. Vital signs: BP 130/85, HR 98, RR 22, SpO2 94% on room air. Chest X-ray shows right lower lobe infiltrate consistent with lobar pneumonia. Diagnosis: Community-acquired pneumonia, right lower lobe. Started on IV ceftriaxone 1g and azithromycin 500mg. Patient admitted for observation and continued antibiotic therapy.

Complete request

import requests

url = "https://{your-endpoint}/v1/codify"
headers = {
    "Authorization": "Bearer YOUR_TOKEN",
    "Content-Type": "application/json",
    "x-doctor": "dr_emergency_001",
    "x-patient": "pt_789"
}

payload = {
    "medical_note": "45-year-old male patient presents to emergency department with fever up to 39°C, productive cough, and shortness of breath for 3 days. Physical examination reveals decreased breath sounds and crackles in right lower lobe. Vital signs: BP 130/85, HR 98, RR 22, SpO2 94% on room air. Chest X-ray shows right lower lobe infiltrate consistent with lobar pneumonia. Diagnosis: Community-acquired pneumonia, right lower lobe. Started on IV ceftriaxone 1g and azithromycin 500mg. Patient admitted for observation and continued antibiotic therapy."
}

response = requests.post(url, json=payload, headers=headers)
result = response.json()

# Filter high confidence codes
high_confidence = [
    a for a in result['final_code_assessments'] 
    if a['confidence_percent'] >= 90
]

print(f"High confidence codes ({len(high_confidence)}):")
for assessment in high_confidence:
    print(f"  {assessment['code']}: {assessment['description']} ({assessment['confidence_percent']}%)")

Response

{
  "final_code_assessments": [
    {
      "code": "J18.1",
      "description": "Lobar pneumonia, unspecified organism",
      "justification": "The chest X-ray confirms right lower lobe infiltrate consistent with lobar pneumonia. The diagnosis explicitly states community-acquired pneumonia affecting the right lower lobe, which corresponds to J18.1.",
      "confidence_percent": 96.8
    },
    {
      "code": "R50.9",
      "description": "Fever, unspecified",
      "justification": "Patient presents with documented fever up to 39°C, which is a significant presenting symptom requiring coding.",
      "confidence_percent": 91.5
    }
  ],
  "discarded_code_assessments": [
    {
      "code": "J15.9",
      "description": "Bacterial pneumonia, unspecified",
      "justification": "While possible, the note doesn't specify bacterial etiology with certainty. J18.1 (lobar pneumonia) is more specific and accurate based on the radiological findings.",
      "confidence_percent": 45.2
    }
  ],
  "run_id": "2af9f1e5-8901-7c4d-9g01-bcdef234567"
}

Interpretation:

J18.1: Lobar pneumonia (96.8% confidence) - Primary diagnosis
R50.9: Fever (91.5% confidence) - Significant presenting symptom
The AI correctly selected lobar pneumonia over unspecified bacterial pneumonia based on radiological evidence

Example 3: PDF file processing

Use case

Process a PDF medical report and extract ICD-10 codes.

Complete request with PDF encoding

import requests
import base64

def encode_pdf(file_path):
    """Encode a PDF file to base64"""
    with open(file_path, 'rb') as file:
        pdf_bytes = file.read()
        base64_data = base64.b64encode(pdf_bytes).decode('utf-8')
    
    return {
        "type": "file",
        "source_type": "base64",
        "data": base64_data,
        "mime_type": "application/pdf",
        "filename": file_path.split('/')[-1]
    }

# Encode the PDF
pdf_file = encode_pdf("medical_report_2025.pdf")

url = "https://{your-endpoint}/v1/codify"
headers = {
    "Authorization": "Bearer YOUR_TOKEN",
    "Content-Type": "application/json",
    "x-doctor": "dr_456",
    "x-patient": "pt_101"
}

payload = {
    "pdf_file": pdf_file,
    "model": "high-quality"
}

response = requests.post(url, json=payload, headers=headers)
result = response.json()

print(f"Processed PDF: {pdf_file['filename']}")
print(f"Final codes: {len(result['final_code_assessments'])}")
for assessment in result['final_code_assessments']:
    print(f"  {assessment['code']}: {assessment['description']} ({assessment['confidence_percent']}%)")

Response

The response format is identical to text input, containing final_code_assessments, discarded_code_assessments, and run_id.

Example 4: FHIR bundle extraction

Use case

Process clinical data already structured in FHIR (Fast Healthcare Interoperability Resources) format and extract simplified diagnostic codes or aggregated information.

The API can also receive FHIR JSON as input to extract/transform specific information.

Input (FHIR Bundle as JSON)

{
  "resourceType": "Bundle",
  "type": "collection",
  "entry": [
    {
      "resource": {
        "resourceType": "Patient",
        "id": "patient-001",
        "name": [{ "family": "Garcia", "given": ["Ana", "Maria"] }],
        "birthDate": "1978-03-15",
        "gender": "female"
      }
    },
    {
      "resource": {
        "resourceType": "Condition",
        "id": "condition-diabetes",
        "clinicalStatus": {
          "coding": [{ "system": "http://terminology.hl7.org/CodeSystem/condition-clinical", "code": "active" }]
        },
        "code": {
          "coding": [
            { "system": "http://snomed.info/sct", "code": "44054006", "display": "Diabetes mellitus type 2" },
            { "system": "http://hl7.org/fhir/sid/icd-10", "code": "E11.9", "display": "Type 2 diabetes mellitus without complications" }
          ]
        },
        "subject": { "reference": "Patient/patient-001" }
      }
    },
    {
      "resource": {
        "resourceType": "Condition",
        "id": "condition-hta",
        "clinicalStatus": {
          "coding": [{ "system": "http://terminology.hl7.org/CodeSystem/condition-clinical", "code": "active" }]
        },
        "code": {
          "coding": [
            { "system": "http://snomed.info/sct", "code": "38341003", "display": "Hypertensive disorder" },
            { "system": "http://hl7.org/fhir/sid/icd-10", "code": "I10", "display": "Essential hypertension" }
          ]
        },
        "subject": { "reference": "Patient/patient-001" }
      }
    },
    {
      "resource": {
        "resourceType": "Observation",
        "id": "obs-glucose",
        "status": "final",
        "code": {
          "coding": [{ "system": "http://loinc.org", "code": "2339-0", "display": "Glucose [Mass/volume] in Blood" }]
        },
        "subject": { "reference": "Patient/patient-001" },
        "effectiveDateTime": "2025-10-19T10:15:00Z",
        "valueQuantity": { "value": 185, "unit": "mg/dL", "system": "http://unitsofmeasure.org", "code": "mg/dL" },
        "interpretation": [
          { "coding": [{ "system": "http://terminology.hl7.org/CodeSystem/v3-ObservationInterpretation", "code": "H", "display": "High" }] }
        ]
      }
    }
  ]
}

JSON Schema for summary extraction

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "fhir_extraction/v1",
  "type": "object",
  "required": ["patient", "active_diagnoses", "relevant_observations"],
  "properties": {
    "patient": {
      "type": "object",
      "properties": {
        "full_name": { "type": "string" },
        "age": { "type": "integer", "description": "Age calculated from birth date" },
        "gender": { "type": "string", "enum": ["male", "female", "other"] }
      }
    },
    "active_diagnoses": {
      "type": "array",
      "description": "Active diagnoses with ICD-10 codes",
      "items": {
        "type": "object",
        "properties": {
          "icd10_code": { "type": "string", "source": "CIE-10-MC-ES-2026" },
          "description": { "type": "string" }
        }
      }
    },
    "relevant_observations": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": { "type": "string", "description": "Observation type (lab, vital sign, etc.)" },
          "name": { "type": "string" },
          "value": { "type": "string" },
          "interpretation": { "type": "string", "description": "Normal, High, Low, etc." }
        }
      }
    },
    "clinical_summary": { "type": "string", "description": "Narrative summary of the encounter" }
  }
}

Request

import json
import requests

fhir_bundle = {
    "resourceType": "Bundle",
    "type": "collection",
    "entry": [
        # ... (the complete FHIR bundle from above)
    ]
}

payload = {
    "input": json.dumps(fhir_bundle, ensure_ascii=False),
    "json_schema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "type": "object",
        "required": ["patient", "active_diagnoses"],
        "properties": {
            "patient": {
                "type": "object",
                "properties": {
                    "full_name": {"type": "string"},
                    "age": {"type": "integer"},
                    "gender": {"type": "string"}
                }
            },
            "active_diagnoses": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "icd10_code": {"type": "string", "source": "CIE-10-MC-ES-2026"},
                        "description": {"type": "string"}
                    }
                }
            },
            "relevant_observations": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "type": {"type": "string"},
                        "name": {"type": "string"},
                        "value": {"type": "string"},
                        "interpretation": {"type": "string"}
                    }
                }
            },
            "clinical_summary": {"type": "string"}
        }
    },
    "configure": "Extract information from the FHIR bundle and simplify to readable format. Keep ICD-10 codes when available.",
    "target_language": "en"
}

response = requests.post(
    "https://{your-endpoint}/v1/codify",
    json=payload,
    headers={
        "Authorization": "Bearer YOUR_TOKEN",
        "x-doctor": "dr_123",
        "x-patient": "pt_001"
    }
)

result = response.json()
print(json.dumps(result['data'], indent=2))

Response

{
  "data": {
    "patient": {
      "full_name": "Ana Maria Garcia",
      "age": 47,
      "gender": "female"
    },
    "active_diagnoses": [
      { "icd10_code": "E11.9", "description": "Type 2 diabetes mellitus without complications" },
      { "icd10_code": "I10", "description": "Essential (primary) hypertension" }
    ],
    "relevant_observations": [
      { "type": "Laboratory", "name": "Blood glucose", "value": "185 mg/dL", "interpretation": "High" }
    ],
    "clinical_summary": "47-year-old female patient with type 2 diabetes mellitus and essential hypertension in follow-up. Latest blood glucose elevated (185 mg/dL). Requires treatment adjustment."
  },
  "run_id": "run_fhir_extract_001"
}

FHIR use cases:

Simplify complex FHIR bundles into readable reports
Extract specific codes (ICD-10, SNOMED, LOINC) from FHIR resources
Generate narrative summaries from structured FHIR data
Convert between FHIR/HL7 versions or normalize codes

Example 5: Structured clinical note extraction

Use case

Extract structured fields from a medical consultation (reason, current illness, treatment plan, vital signs, etc.) for automatic EHR documentation.

Complete request

import requests

payload = {
    "input": "29-year-old female patient with odynophagia, fever 38.5C for 2 days. Examination: tonsillar plaques, tender cervical lymphadenopathy, no rash. Treatment: ibuprofen and rest. Antigen test suggested.",
    "json_schema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "title": "clinical_note/v1",
        "type": "object",
        "required": ["chief_complaint", "present_illness", "treatment_plan"],
        "properties": {
            "current_date": {"type": "string"},
            "next_appointment": {"type": "string"},
            "chief_complaint": {"type": "string"},
            "summary": {"type": "string"},
            "present_illness": {"type": "string"},
            "observations": {"type": "string"},
            "treatment_plan": {"type": "string"},
            "orders": {"type": "string"},
            "vital_signs": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "name": {"type": "string"},
                        "value": {"type": "string"},
                        "unit": {"type": "string"},
                        "timestamp": {"type": "string", "format": "date-time"}
                    }
                }
            }
        }
    },
    "target_language": "en"
}

response = requests.post(
    "https://{your-endpoint}/v1/codify",
    json=payload,
    headers={
        "Authorization": "Bearer YOUR_TOKEN",
        "x-doctor": "dr_234",
        "x-patient": "pt_567"
    }
)

result = response.json()
print(result['data'])

Response

{
  "data": {
    "current_date": "2025-10-19T09:45:00Z",
    "next_appointment": "",
    "chief_complaint": "Sore throat and fever for 2 days",
    "summary": "Probable pharyngotonsillitis; started on ibuprofen, rest and hydration; consider antigen test.",
    "present_illness": "Pharyngeal pain, fever 38.5C, odynophagia. Tonsillar plaques, cervical lymphadenopathy.",
    "observations": "Good general condition, no rash.",
    "treatment_plan": "Ibuprofen 400mg every 8h for 3 days; rest; hydration.",
    "orders": "Rapid streptococcal antigen test if available.",
    "vital_signs": [
      { "name": "temperature", "value": "38.5", "unit": "C", "timestamp": "2025-10-19T09:40:00Z" }
    ]
  },
  "run_id": "run_def789"
}

Example 6: Oncology registry extraction

Use case

Automatically populate an oncology registry with data extracted from clinical notes.

Complete request

import requests

payload = {
    "input": "Patient diagnosed with left breast adenocarcinoma. Stage T2N1M0 (stage IIA). Hormone receptors positive (ER+, PR+), HER2 negative. Neoadjuvant chemotherapy protocol AC-T (adriamycin, cyclophosphamide followed by taxanes) initiated. Breast-conserving surgery planned after favorable response to chemotherapy.",
    "json_schema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "title": "oncology_registry/v1",
        "type": "object",
        "required": ["diagnosis", "location", "tnm_staging"],
        "properties": {
            "diagnosis": {"type": "string", "description": "Histological diagnosis"},
            "icd10_code": {"type": "string", "source": "CIE-10-MC-ES-2026"},
            "location": {"type": "string", "description": "Anatomical tumor location"},
            "tnm_staging": {
                "type": "object",
                "properties": {
                    "t": {"type": "string"},
                    "n": {"type": "string"},
                    "m": {"type": "string"},
                    "stage": {"type": "string"}
                }
            },
            "biomarkers": {
                "type": "object",
                "properties": {
                    "estrogen_receptors": {"type": "string"},
                    "progesterone_receptors": {"type": "string"},
                    "her2": {"type": "string"}
                }
            },
            "planned_treatment": {"type": "string"},
            "chemotherapy_protocol": {"type": "string"}
        }
    },
    "target_language": "en"
}

response = requests.post(
    "https://{your-endpoint}/v1/codify",
    json=payload,
    headers={
        "Authorization": "Bearer YOUR_TOKEN",
        "x-doctor": "dr_345",
        "x-patient": "pt_678"
    }
)

result = response.json()
print(result['data'])

Response

{
  "data": {
    "diagnosis": "Breast adenocarcinoma",
    "icd10_code": "C50.9",
    "location": "left breast",
    "tnm_staging": { "t": "T2", "n": "N1", "m": "M0", "stage": "IIA" },
    "biomarkers": {
      "estrogen_receptors": "positive",
      "progesterone_receptors": "positive",
      "her2": "negative"
    },
    "planned_treatment": "Neoadjuvant chemotherapy followed by breast-conserving surgery",
    "chemotherapy_protocol": "AC-T (adriamycin, cyclophosphamide followed by taxanes)"
  },
  "run_id": "run_onco_001"
}

Example 7: Medication extraction

Use case

Extract prescribed medications with pharmacy system codes.

Complete request

import requests

payload = {
    "input": "Prescribed enalapril 10mg every 12 hours orally, atorvastatin 20mg nightly orally, and metformin 850mg every 8 hours with meals. Continue with aspirin 100mg at breakfast.",
    "json_schema": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "type": "object",
        "properties": {
            "medications": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "name": {"type": "string"},
                        "pharmacy_code": {"type": "string", "source": "BOTPLUS-2024"},
                        "dosage": {"type": "string"},
                        "frequency": {"type": "string"},
                        "route": {"type": "string", "enum": ["oral", "iv", "im", "sc", "topical", "inhaled"]},
                        "special_instructions": {"type": "string"}
                    }
                }
            }
        }
    },
    "configure": "Extract all medications with exact pharmacy codes. Do not invent codes.",
    "target_language": "en"
}

response = requests.post(
    "https://{your-endpoint}/v1/codify",
    json=payload,
    headers={
        "Authorization": "Bearer YOUR_TOKEN",
        "x-doctor": "dr_456",
        "x-patient": "pt_789"
    }
)

result = response.json()
print(result['data'])

Response

{
  "data": {
    "medications": [
      { "name": "Enalapril", "pharmacy_code": "652302", "dosage": "10 mg", "frequency": "every 12 hours", "route": "oral", "special_instructions": "" },
      { "name": "Atorvastatin", "pharmacy_code": "663798", "dosage": "20 mg", "frequency": "nightly", "route": "oral", "special_instructions": "" },
      { "name": "Metformin", "pharmacy_code": "651077", "dosage": "850 mg", "frequency": "every 8 hours", "route": "oral", "special_instructions": "with meals" },
      { "name": "Acetylsalicylic acid", "pharmacy_code": "651449", "dosage": "100 mg", "frequency": "every 24 hours", "route": "oral", "special_instructions": "at breakfast" }
    ]
  },
  "run_id": "run_med_001"
}

Example 8: Batch processing multiple notes

Use case

Process multiple clinical notes in parallel for efficient bulk coding.

Complete request

import requests
import concurrent.futures

API_URL = "https://{your-endpoint}/v1/codify"
API_TOKEN = "YOUR_TOKEN"

headers = {
    "Authorization": f"Bearer {API_TOKEN}",
    "Content-Type": "application/json"
}

def codify_note(note_data):
    """Process a single medical note"""
    payload = {
        "medical_note": note_data['text'],
        "model": "balanced"
    }
    
    # Add optional tracking headers
    request_headers = headers.copy()
    if 'doctor_id' in note_data:
        request_headers['x-doctor'] = note_data['doctor_id']
    if 'patient_id' in note_data:
        request_headers['x-patient'] = note_data['patient_id']
    
    response = requests.post(API_URL, json=payload, headers=request_headers)
    return {
        'note_id': note_data['id'],
        'result': response.json()
    }

# Sample notes to process
notes = [
    {
        'id': 'note_001',
        'text': 'Patient with Type 2 Diabetes, controlled with Metformin.',
        'doctor_id': 'dr_123',
        'patient_id': 'pt_456'
    },
    {
        'id': 'note_002',
        'text': 'Hypertension, essential, well controlled on Lisinopril 10mg daily.',
        'doctor_id': 'dr_124',
        'patient_id': 'pt_457'
    },
    {
        'id': 'note_003',
        'text': 'Acute bronchitis with productive cough. Started on azithromycin.',
        'doctor_id': 'dr_125',
        'patient_id': 'pt_458'
    }
]

# Process in parallel (max 10 concurrent requests)
def process_batch(notes, max_workers=10):
    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
        futures = [executor.submit(codify_note, note) for note in notes]
        results = []
        for future in concurrent.futures.as_completed(futures):
            try:
                results.append(future.result())
            except Exception as e:
                print(f"Error processing note: {e}")
        return results

# Execute batch processing
results = process_batch(notes)

# Display results
for result in results:
    print(f"\nNote ID: {result['note_id']}")
    print(f"Codes found: {len(result['result']['final_code_assessments'])}")
    for assessment in result['result']['final_code_assessments']:
        print(f"  - {assessment['code']}: {assessment['description']} ({assessment['confidence_percent']}%)")

Output

Note ID: note_001
Codes found: 2
  - E11.9: Type 2 diabetes mellitus without complications (94.5%)
  - Z79.84: Long term (current) use of oral hypoglycemic drugs (97.2%)

Note ID: note_002
Codes found: 1
  - I10: Essential (primary) hypertension (96.8%)

Note ID: note_003
Codes found: 1
  - J20.9: Acute bronchitis, unspecified (93.4%)

Best practices from examples

1. Use tracking headers

Always include x-doctor and x-patient headers for better audit trails:

headers = {
    "Authorization": "Bearer YOUR_TOKEN",
    "x-doctor": "dr_123",
    "x-patient": "pt_456"
}

2. Filter by confidence threshold

Review codes with high confidence first:

high_confidence = [
    a for a in result['final_code_assessments']
    if a['confidence_percent'] >= 85
]

3. Review discarded codes

Understand the AI’s decision-making process:

for assessment in result['discarded_code_assessments']:
    print(f"Rejected: {assessment['code']} - {assessment['justification']}")

4. Store run_id for debugging

Always save the run_id for troubleshooting:

database.save_coding_result({
    'patient_id': 'pt_456',
    'codes': result['final_code_assessments'],
    'run_id': result['run_id'],
    'timestamp': datetime.now()
})

5. Handle errors gracefully

try:
    response = requests.post(url, json=payload, headers=headers)
    response.raise_for_status()
    result = response.json()
except requests.exceptions.HTTPError as e:
    if e.response.status_code == 429:
        # Rate limit - implement backoff
        time.sleep(60)
        retry()
    elif e.response.status_code == 413:
        # Payload too large - split the note
        split_and_process()
    else:
        # Log and handle other errors
        logger.error(f"API error: {e.response.json()}")

Next steps

Error Handling

Learn about error codes and retry strategies

Request Structure

Complete field reference

Authentication

API tokens and security

Quick Start

Get started in 5 minutes

​Example 1: Simple diabetes case

​Use case

​Input text

​Complete request

​Response

​Example 2: Emergency pneumonia case

​Use case

​Input text

​Complete request

​Response

​Example 3: PDF file processing

​Use case

​Complete request with PDF encoding

​Response

​Example 4: FHIR bundle extraction

​Use case

​Input (FHIR Bundle as JSON)

​JSON Schema for summary extraction

​Request

​Response

​Example 5: Structured clinical note extraction

​Use case

​Complete request

​Response

​Example 6: Oncology registry extraction

​Use case

​Complete request

​Response

​Example 7: Medication extraction

​Use case

​Complete request

​Response

​Example 8: Batch processing multiple notes

​Use case

​Complete request

​Output

​Best practices from examples

​1. Use tracking headers

​2. Filter by confidence threshold

​3. Review discarded codes

​4. Store run_id for debugging

​5. Handle errors gracefully

​Next steps

Error Handling

Request Structure

Authentication

Quick Start

Example 1: Simple diabetes case

Use case

Input text

Complete request

Response

Example 2: Emergency pneumonia case

Use case

Input text

Complete request

Response

Example 3: PDF file processing

Use case

Complete request with PDF encoding

Response

Example 4: FHIR bundle extraction

Use case

Input (FHIR Bundle as JSON)

JSON Schema for summary extraction

Request

Response

Example 5: Structured clinical note extraction

Use case

Complete request

Response

Example 6: Oncology registry extraction

Use case

Complete request

Response

Example 7: Medication extraction

Use case

Complete request

Response

Example 8: Batch processing multiple notes

Use case

Complete request

Output

Best practices from examples

1. Use tracking headers

2. Filter by confidence threshold

3. Review discarded codes

4. Store run_id for debugging

5. Handle errors gracefully

Next steps