| name | deepread-medical |
| title | DeepRead Medical Records |
| description | Extract structured data from medical records, lab reports, prescriptions, and clinical documents. Pre-built schemas for patient info, diagnoses, medications, vitals. PII redaction for HIPAA compliance. 97%+ accuracy. Free 2,000 pages/month. |
| metadata | {"openclaw":{"requires":{"env":["DEEPREAD_API_KEY"]},"primaryEnv":"DEEPREAD_API_KEY","homepage":"https://www.deepread.tech"}} |
DeepRead Medical Records Processing
Extract structured data from medical records, lab reports, prescriptions, discharge summaries, and clinical documents. Then redact patient PII for HIPAA-compliant sharing, archiving, or LLM processing.
This skill instructs the agent to POST documents to https://api.deepread.tech and poll for results. No system files are modified.
What You Get Back
Submit a medical record and get structured JSON. Extracted fields come back as a list under extraction.fields[] (each field has key, value, needs_review, and location.page):
{
"schema_version": "dp02",
"status": "completed",
"extraction": {
"fields": [
{"key": "patient_name", "value": "Jane Smith", "needs_review": false, "location": {"page": 1}},
{"key": "date_of_birth", "value": "1990-03-15", "needs_review": false, "location": {"page": 1}},
{"key": "mrn", "value": "MRN-2026-004521", "needs_review": false, "location": {"page": 1}},
{"key": "visit_date", "value": "2026-03-28", "needs_review": false, "location": {"page": 1}},
{"key": "provider", "value": "Dr. Sarah Chen, MD", "needs_review": false, "location": {"page": 1}},
{"key": "diagnoses", "value": [
{"code": "J06.9", "description": "Acute upper respiratory infection"},
{"code": "R50.9", "description": "Fever, unspecified"}
], "needs_review": false, "location": {"page": 2}},
{"key": "medications", "value": [
{"name": "Amoxicillin", "dosage": "500mg", "frequency": "3x daily", "duration": "10 days"},
{"name": "Acetaminophen", "dosage": "500mg", "frequency": "as needed", "duration": "PRN"}
], "needs_review": false, "location": {"page": 2}},
{"key": "vitals", "value": {
"blood_pressure": "128/82",
"heart_rate": 88,
"temperature": 101.2,
"weight": "165 lbs"
}, "needs_review": false, "location": {"page": 1}},
{"key": "follow_up", "value": "Return in 2 weeks if symptoms persist", "needs_review": true, "review_reason": "Handwritten note, partial OCR", "location": {"page": 1}}
]
}
}
Fields with needs_review: true need human review (check review_reason). Everything else is high-confidence.
Setup
Get Your API Key
open "https://www.deepread.tech/dashboard/?utm_source=clawhub"
Save it:
export DEEPREAD_API_KEY="sk_live_your_key_here"
Medical Record Schema
Use this pre-built schema for clinical documents. Customize it for your specific document types:
{
"type": "object",
"properties": {
"patient_name": {"type": "string", "description": "Patient full name"},
"date_of_birth": {"type": "string", "description": "Patient date of birth (YYYY-MM-DD)"},
"mrn": {"type": "string", "description": "Medical record number or patient ID"},
"visit_date": {"type": "string", "description": "Date of visit or service (YYYY-MM-DD)"},
"provider": {"type": "string", "description": "Treating physician or provider name with credentials"},
"facility": {"type": "string", "description": "Hospital or clinic name"},
"diagnoses": {
"type": "array",
"items": {
"type": "object",
"properties": {
"code": {"type": "string", "description": "ICD-10 diagnosis code if present"},
"description": {"type": "string", "description": "Diagnosis description"}
}
},
"description": "List of diagnoses or conditions"
},
"medications": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {"type": "string", "description": "Medication name"},
"dosage": {"type": "string", "description": "Dosage amount and unit"},
"frequency": {"type": "string", "description": "How often taken"},
"duration": {"type": "string", "description": "Duration of treatment"}
}
},
"description": "Prescribed or current medications"
},
"vitals": {
"type": "object",
"properties": {
"blood_pressure": {"type": "string", "description": "Blood pressure reading (systolic/diastolic)"},
"heart_rate": {"type": "number", "description": "Heart rate in BPM"},
"temperature": {"type": "number", "description": "Body temperature in Fahrenheit"},
"weight": {"type": "string", "description": "Patient weight with unit"},
"height": {"type": "string", "description": "Patient height with unit"}
},
"description": "Vital signs recorded during visit"
},
"procedures": {
"type": "array",
"items": {
"type": "object",
"properties": {
"code": {"type": "string", "description": "CPT procedure code if present"},
"description": {"type": "string", "description": "Procedure description"}
}
},
"description": "Procedures performed"
},
"lab_results": {
"type": "array",
"items": {
"type": "object",
"properties": {
"test_name": {"type": "string", "description": "Lab test name"},
"value": {"type": "string", "description": "Result value with unit"},
"reference_range": {"type": "string", "description": "Normal reference range"},
"flag": {"type": "string", "description": "H (high), L (low), or normal"}
}
},
"description": "Laboratory test results"
},
"allergies": {
"type": "array",
"items": {"type": "string"},
"description": "Known allergies"
},
"follow_up": {"type": "string", "description": "Follow-up instructions or next appointment"},
"notes": {"type": "string", "description": "Clinical notes or additional observations"}
}
}
Extract Data From Medical Records
Python
import requests
import json
import time
API_KEY = "sk_live_YOUR_KEY"
BASE = "https://api.deepread.tech"
headers = {"X-API-Key": API_KEY}
schema = json.dumps({
"type": "object",
"properties": {
"patient_name": {"type": "string", "description": "Patient full name"},
"date_of_birth": {"type": "string", "description": "Patient DOB (YYYY-MM-DD)"},
"mrn": {"type": "string", "description": "Medical record number"},
"provider": {"type": "string", "description": "Treating physician with credentials"},
"diagnoses": {
"type": "array",
"items": {"type": "object", "properties": {
"code": {"type": "string", "description": "ICD-10 code"},
"description": {"type": "string"}
}},
"description": "Diagnoses"
},
"medications": {
"type": "array",
"items": {"type": "object", "properties": {
"name": {"type": "string"},
"dosage": {"type": "string"},
"frequency": {"type": "string"}
}},
"description": "Medications"
},
"lab_results": {
"type": "array",
"items": {"type": "object", "properties": {
"test_name": {"type": "string"},
"value": {"type": "string"},
"reference_range": {"type": "string"},
"flag": {"type": "string", "description": "H, L, or normal"}
}},
"description": "Lab results"
}
}
})
with open("patient_record.pdf", "rb") as f:
job = requests.post(
f"{BASE}/v1/process",
headers=headers,
files={"file": f},
data={"schema": schema},
).json()
job_id = job["id"]
print(f"Processing: {job_id}")
delay = 5
while True:
time.sleep(delay)
result = requests.get(f"{BASE}/v1/jobs/{job_id}", headers=headers).json()
if result["status"] == "completed":
fields = result.get("extraction", {}).get("fields", [])
print(json.dumps(fields, indent=2))
for f in fields:
if f.get("needs_review"):
print(f"\n REVIEW: {f['key']} — {f.get('review_reason')}")
break
elif result["status"] == "failed":
print(f"Failed: {result.get('error')}")
break
delay = min(delay * 1.5, 15)
cURL
curl -s -X POST https://api.deepread.tech/v1/process \
-H "X-API-Key: $DEEPREAD_API_KEY" \
-F "file=@patient_record.pdf" \
-F 'schema={"type":"object","properties":{"patient_name":{"type":"string","description":"Patient full name"},"mrn":{"type":"string","description":"Medical record number"},"diagnoses":{"type":"array","items":{"type":"object","properties":{"code":{"type":"string","description":"ICD-10 code"},"description":{"type":"string"}}},"description":"Diagnoses"},"medications":{"type":"array","items":{"type":"object","properties":{"name":{"type":"string"},"dosage":{"type":"string"},"frequency":{"type":"string"}}},"description":"Medications"}}}'
HIPAA Compliance: Redact Patient PII
After extracting data, redact patient PII from the original document before sharing, archiving, or sending to an LLM:
with open("patient_record.pdf", "rb") as f:
extract_job = requests.post(
f"{BASE}/v1/process",
headers=headers,
files={"file": f},
data={"schema": schema},
).json()
with open("patient_record.pdf", "rb") as f:
redact_job = requests.post(
f"{BASE}/v1/pii/redact",
headers=headers,
files={"file": f},
).json()
redact_id = redact_job["id"]
delay = 5
while True:
time.sleep(delay)
result = requests.get(f"{BASE}/v1/pii/{redact_id}", headers=headers).json()
if result["status"] == "completed":
report = result["report"]
print(f"Redacted {report['total_redactions']} PII instances")
for pii_type, info in report["pii_detected"].items():
print(f" {pii_type}: {info['count']} found")
pdf = requests.get(result["redacted_file_url"]).content
with open("patient_record_redacted.pdf", "wb") as f:
f.write(pdf)
print("Saved: patient_record_redacted.pdf")
break
elif result["status"] == "failed":
print(f"Failed: {result.get('error')}")
break
delay = min(delay * 1.5, 15)
DeepRead's PII redaction is context-aware for medical documents:
- "Dr. Sarah Chen" on letterhead — physician, SKIP
- "Sarah Chen" on intake form — patient, REDACT
- "admissions@hospital.edu" — institutional email, SKIP
- "john.smith@gmail.com" — personal email, REDACT
- Invoice date — not a DOB, SKIP
- "Date of Birth: 03/15/1990" — DOB, REDACT
14 PII types detected: names, SSNs, DOB, medical record numbers, phone, email, address, credit cards, passport, driver's license, bank accounts, IBANs, IPs, URLs.
Use Cases
- Clinical Data Extraction — Pull diagnoses, medications, vitals, and lab results from patient records into your EHR or data warehouse
- Lab Report Processing — Extract test names, values, reference ranges, and abnormal flags from lab reports
- Prescription Digitization — Convert handwritten or printed prescriptions into structured medication data
- Insurance Claims — Extract procedure codes, diagnosis codes, and patient info for claims processing
- Medical Research — De-identify patient records for research datasets while preserving clinical data
- Discharge Summary Processing — Extract follow-up instructions, medications, and diagnoses from discharge paperwork
- Prior Authorization — Pull relevant clinical data to auto-fill prior auth forms
Tips for Medical Documents
- Use ICD-10/CPT code descriptions — Adding "ICD-10 diagnosis code if present" in the schema description helps the model recognize code formats
- Specify units — "Temperature in Fahrenheit" or "Weight with unit (lbs or kg)" reduces ambiguity
- Create blueprints for recurring form types — If you process the same lab report format repeatedly, train a blueprint at deepread.tech/dashboard/optimizer for 20-30% improvement
- Always redact before sharing — Use the PII redaction endpoint before sending documents to LLMs, external systems, or non-clinical staff
BYOK — Zero Processing Costs
Connect your own OpenAI, Google, or OpenRouter key via the dashboard. All document processing routes through your provider — zero DeepRead LLM costs, page quota skipped.
Set it up: https://www.deepread.tech/dashboard/byok
Related DeepRead Skills
- deepread-ocr — General OCR and structured extraction —
clawhub install uday390/deepread-ocr
- deepread-pii — Redact PII from any document —
clawhub install uday390/deepread-pii
- deepread-form-fill — Fill PDF forms with AI vision —
clawhub install uday390/deepread-form-fill
- deepread-invoice — Invoice and receipt processing —
clawhub install uday390/deepread-invoice
- deepread-agent-setup — OAuth device flow authentication —
clawhub install uday390/deepread-agent-setup
- deepread-byok — Bring Your Own Key setup —
clawhub install uday390/deepread-byok
Support
Get started free: https://www.deepread.tech/dashboard/?utm_source=clawhub
