Telemedicine and digital health platforms generate enormous volumes of unstructured medical data: clinical notes, patient messages, lab reports, medication histories, and remote monitoring feeds. Chinese LLMs like DeepSeek V4, GLM-4, and Qwen3 can process this data to automate documentation, enhance clinical decision support, and improve patient communication, all while maintaining strict privacy standards. TokenEase's unified API provides healthcare organizations with cost-effective access to these powerful models.
Transform physician-patient conversations and consultation notes into structured clinical records, discharge summaries, and referral letters.
import requests
def generate_clinical_note(consultation_transcript, patient_history, note_type):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "You are a medical documentation specialist. Generate accurate, structured clinical notes following standard medical terminology and formats."},
{"role": "user", "content": f"Patient history: {patient_history}\nNote type: {note_type}\nTranscript:\n{consultation_transcript}\n\nGenerate structured clinical note."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
transcript = """
Dr: What brings you in today?
Pt: I've had chest pain for the past 3 days. It's worse when I exercise.
Dr: Any shortness of breath?
Pt: Yes, especially climbing stairs. No fever.
Dr: BP 140/90, HR 88, lungs clear. ECG shows ST depression in V4-V6.
"""
history = "58-year-old male, hypertension, hyperlipidemia, former smoker (quit 5 years ago)"
note = generate_clinical_note(transcript, history, "SOAP progress note")
Analyze patient-reported symptoms and history to suggest appropriate urgency levels and specialty referrals.
def triage_patient(symptoms, demographics, vital_signs):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "You are a clinical triage nurse. Assess patient symptoms and recommend urgency level (Emergency/Urgent/Routine), suggested specialty, and initial guidance. Include appropriate disclaimers."},
{"role": "user", "content": f"Demographics: {demographics}\nVitals: {vital_signs}\nSymptoms:\n{symptoms}\n\nProvide triage assessment."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
symptoms = "Severe headache for 6 hours, worst of life, neck stiffness, photophobia, no fever. No trauma."
demo = "32-year-old female, otherwise healthy, no medications"
vitals = "BP 145/95, HR 110, Temp 37.1C, O2 sat 98%"
triage = triage_patient(symptoms, demo, vitals)
Check for drug-drug interactions, contraindications, and allergy conflicts across complex medication regimens.
def check_medication_interactions(medication_list, allergies, conditions):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "glm-4-plus",
"messages": [
{"role": "system", "content": "Check medication interactions and contraindications. Flag serious interactions, note monitoring requirements, and suggest alternatives if needed."},
{"role": "user", "content": f"Conditions: {conditions}\nAllergies: {allergies}\nMedications:\n{medication_list}\n\nCheck interactions and provide recommendations."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
meds = """
- Warfarin 5mg daily
- Amiodarone 200mg daily
- Atorvastatin 40mg daily
- Omeprazole 20mg daily
- Acetaminophen PRN
"""
allergies = "Sulfa drugs, penicillin"
conditions = "Atrial fibrillation, hyperlipidemia, GERD"
interactions = check_medication_interactions(meds, allergies, conditions)
Generate concise patient summaries from lengthy EHR records for handoffs, referrals, and care coordination.
def summarize_patient_record(ehr_text, purpose, audience):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "kimi-k2",
"messages": [
{"role": "system", "content": f"Summarize patient health records for {audience}. Highlight critical information, active problems, and care gaps."},
{"role": "user", "content": f"Purpose: {purpose}\nAudience: {audience}\nEHR:\n{ehr_text}\n\nGenerate summary."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
ehr = """
2026-01-15: Type 2 diabetes diagnosed, HbA1c 8.2%. Started metformin 500mg BID.
2026-03-20: HbA1c 7.5%. Added glipizide 5mg daily.
2026-05-10: Foot ulcer right heel, referred to podiatry.
2026-06-15: HbA1c 7.1%. Ulcer healing well.
2026-08-01: BP elevated 155/95 at home. No chest pain.
Medications: Metformin, glipizide, lisinopril 10mg, atorvastatin 20mg
Allergies: NKDA
"""
summary = summarize_patient_record(ehr, "Endocrinology referral", "specialist physician")
Interpret alerts from wearable devices and remote monitoring systems, distinguishing true concerns from false alarms.
def interpret_monitoring_alert(alert_data, patient_context, device_type):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "Interpret remote monitoring alerts. Assess clinical significance, suggest actions, and determine if provider notification is warranted."},
{"role": "user", "content": f"Device: {device_type}\nPatient: {patient_context}\nAlert:\n{alert_data}\n\nInterpret and recommend action."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
alert = """
Device: Continuous Glucose Monitor
Alert: Glucose 65 mg/dL, trend arrow down
Duration: 15 minutes below 70
Pattern: Post-prandial drop 2 hours after lunch
"""
patient = "45-year-old female, T2DM on metformin + basal insulin, no history of severe hypoglycemia"
interpretation = interpret_monitoring_alert(alert, patient, "CGM")
Translate medical documents, discharge instructions, and patient education materials with clinical accuracy.
def translate_medical_content(source_text, target_language, content_type):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": f"Translate medical content to {target_language}. Preserve clinical accuracy, use standard medical terminology, and maintain appropriate reading level for patients."},
{"role": "user", "content": f"Content type: {content_type}\nText:\n{source_text}\n\nTranslate accurately."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
text = """
DISCHARGE INSTRUCTIONS:
- Take amoxicillin 500mg three times daily for 7 days
- Follow up with primary care physician in 3-5 days
- Return to ER if fever > 101F, worsening pain, or difficulty breathing
- Wound care: Keep incision clean and dry, change dressing daily
"""
translation = translate_medical_content(text, "Spanish", "discharge instructions")
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