Medical scribe

1
import requests
2
import time
3
4
# ── Config ────────────────────────────────────────────────────
5
base_url = "https://api.assemblyai.com"
6
headers = {"authorization": "YOUR_API_KEY"}
7
8
audio_url = "https://assembly.ai/wildfires.mp3"  # Replace with your clinical audio
9
10
# ── Step 1: Transcribe with Medical Mode + speaker labels + entity detection ──
11
data = {
12
    "audio_url": audio_url,
13
    "speech_models": ["universal-3-pro-medical"],
14
    "speaker_labels": True,
15
    "entity_detection": True,
16
}
17
18
response = requests.post(base_url + "/v2/transcript", headers=headers, json=data)
19
response.raise_for_status()
20
transcript_id = response.json()["id"]
21
22
while True:
23
    result = requests.get(f"{base_url}/v2/transcript/{transcript_id}", headers=headers).json()
24
    if result["status"] == "completed":
25
        break
26
    elif result["status"] == "error":
27
        raise RuntimeError(f"Transcription failed: {result['error']}")
28
    time.sleep(3)
29
30
# ── Step 2: Identify speakers by role ──
31
understanding_response = requests.post(
32
    "https://llm-gateway.assemblyai.com/v1/understanding",
33
    headers=headers,
34
    json={
35
        "transcript_id": transcript_id,
36
        "speech_understanding": {
37
            "request": {
38
                "speaker_identification": {
39
                    "speaker_type": "role",
40
                    "known_values": ["Provider", "Patient"],
41
                }
42
            }
43
        },
44
    },
45
)
46
understanding_response.raise_for_status()
47
identified = understanding_response.json()
48
49
# ── Step 3: Extract detected entities ──
50
entities = result.get("entities", [])
51
entity_summary = "\n".join(
52
    f"- {e['entity_type']}: {e['text']}" for e in entities
53
)
54
55
# ── Step 4: Format identified transcript ──
56
speaker_transcript = "\n".join(
57
    f"{u['speaker']}: {u['text']}" for u in identified["utterances"]
58
)
59
60
# ── Step 5: Generate SOAP note via LLM Gateway ──
61
llm_response = requests.post(
62
    "https://llm-gateway.assemblyai.com/v1/chat/completions",
63
    headers=headers,
64
    json={
65
        "model": "claude-sonnet-4-5-20250929",
66
        "messages": [
67
            {
68
                "role": "user",
69
                "content": (
70
                    "You are a medical scribe. Given the clinical encounter transcript and "
71
                    "detected entities below, generate a structured SOAP note.\n\n"
72
                    "Format the note with these sections:\n"
73
                    "- **Subjective**: Patient's reported symptoms and history\n"
74
                    "- **Objective**: Clinical observations and measurements\n"
75
                    "- **Assessment**: Diagnosis or clinical impression\n"
76
                    "- **Plan**: Treatment plan, prescriptions, and follow-up\n\n"
77
                    f"Detected entities:\n{entity_summary}\n\n"
78
                    f"Transcript:\n{speaker_transcript}"
79
                ),
80
            }
81
        ],
82
        "max_tokens": 2000,
83
    },
84
)
85
llm_response.raise_for_status()
86
87
print("=== SOAP Note ===\n")
88
print(llm_response.json()["choices"][0]["message"]["content"])