# Deepread Medical > 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. - Skill: `deepread-tech/deepread-medical` (Agent Skill) - Install (CLI): `npx skillmds@latest add deepread-tech/deepread-medical` - Raw SKILL.md: https://api.skillmd.com/api/skills/deepread-tech/deepread-medical/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: deepread-tech (https://skillmd.com/u/deepread-tech) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/deepread-tech/deepread-medical --- # 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`): ```json { "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 ```bash open "https://www.deepread.tech/dashboard/?utm_source=clawhub" ``` Save it: ```bash 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: ```json { "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 ```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" } } }) # Submit medical record 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}") # Poll for results 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)) # Flag fields needing review 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 ```bash 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: ```python # Step 1: Extract structured data (keep the data you need) 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() # Step 2: Redact PII from the original (clean copy for filing) with open("patient_record.pdf", "rb") as f: redact_job = requests.post( f"{BASE}/v1/pii/redact", headers=headers, files={"file": f}, ).json() # Poll for redaction results 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") # Download redacted file 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 - **Dashboard**: https://www.deepread.tech/dashboard - **Demo Repo**: https://github.com/deepread-tech/deepread-demo - **Issues**: https://github.com/deepread-tech/deep-read-service/issues - **Email**: support@deepread.tech --- **Get started free:** https://www.deepread.tech/dashboard/?utm_source=clawhub