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Medical Record Redaction
99.7% Accuracy
70+ Data Types

Medical Record Redaction

Comprehensive PHI detection and de-identification for healthcare data including patient identifiers, clinical notes, and medical terminology.

Enterprise Security
Real-Time Processing
Compliance Ready
0 Words Protected
0+ Enterprise Clients
0+ Languages
18
HIPAA Identifiers
99.2 %
Detection Accuracy
50 M+
Records Processed
<100 ms
Avg Processing

Powerful Redaction Features

Everything you need for comprehensive data protection

HIPAA Safe Harbor

Automatically identify and remove all 18 HIPAA identifiers to achieve Safe Harbor de-identification standard compliance.

Patient Identifier Detection

Detect MRNs, patient names, dates of birth, Social Security numbers, and other patient identifiers across clinical documents.

Clinical Note Processing

Parse unstructured clinical notes including progress notes, discharge summaries, and operative reports with medical NLP.

Medical Terminology Aware

Understand medical context to distinguish clinical terms from PHI, preserving diagnostic and treatment information.

Healthcare Format Support

Process HL7 v2/v3, FHIR, CCD/C-CDA, and DICOM structured reports with format-aware redaction.

Audit Trail Documentation

Comprehensive logging for compliance verification, demonstrating de-identification methodology for audits.

Protecting Patient Information in Medical Records

Healthcare organizations handle vast amounts of Protected Health Information (PHI) that requires de-identification for research, analytics, and secondary use. RedactionAPI provides comprehensive PHI detection across clinical documents, supporting HIPAA Safe Harbor compliance while preserving the clinical value of medical data.

HIPAA De-Identification Standards

HIPAA provides two methods for de-identifying protected health information: Safe Harbor and Expert Determination. RedactionAPI supports both approaches:

Safe Harbor Method

Remove all 18 specified identifiers with no actual knowledge that remaining information could identify an individual.

Our default healthcare profile implements complete Safe Harbor compliance.

Expert Determination

Statistical or scientific methods determine very small re-identification risk. Allows retention of some identifiers.

Configurable profiles support custom de-identification approaches.

The 18 HIPAA Identifiers

Safe Harbor requires removal of these 18 types of identifiers:

# Identifier Type Examples Detection Method
1 Names Patient, family members, providers NLP + name database
2 Geographic data Addresses, cities, zip codes Pattern + geo database
3 Dates Birth, admission, discharge, death Date pattern recognition
4 Phone numbers Home, work, mobile, fax Pattern validation
5 Fax numbers All fax numbers Pattern + context
6 Email addresses Patient email Email pattern
7 Social Security numbers SSN, tax ID Pattern + validation
8 Medical record numbers MRN, patient ID Context + pattern
9 Health plan numbers Insurance member ID Context + pattern
10 Account numbers Billing, financial accounts Context + pattern
11 Certificate/license numbers Driver's license, professional license Pattern + validation
12 Vehicle identifiers License plates, VINs Pattern recognition
13 Device identifiers Medical device serial numbers Context + pattern
14 Web URLs Patient portal links, personal websites URL pattern
15 IP addresses Network identifiers IP pattern
16 Biometric identifiers Fingerprints, voiceprints Context + keywords
17 Full face photos Patient photographs Image detection
18 Any other unique identifier Tattoo descriptions, unique characteristics Context + NLP

Clinical Document Processing

Medical records include both structured data and unstructured clinical notes. Our medical NLP handles both:

Clinical Note Example

Original Clinical Note

PATIENT: John Smith          MRN: 12345678
DOB: 03/15/1965              DOS: 01/15/2024

CHIEF COMPLAINT: 58-year-old male presents with chest pain x 2 days.

HISTORY OF PRESENT ILLNESS:
Mr. Smith is a 58-year-old male who presents to the ED with complaints
of substernal chest pain that began 2 days ago. Patient states pain
radiates to his left arm. He was previously seen at Memorial Hospital
on 01/13/2024. His wife Mary Smith drove him to the ED today.

Patient lives at 123 Oak Street, Springfield, IL 62701. He can be
reached at (555) 123-4567. His insurance ID is BCBS-987654321.

ASSESSMENT/PLAN:
1. Acute coronary syndrome - will admit to cardiology
2. Contact cardiologist Dr. Johnson at ext. 4321

De-Identified Output (Safe Harbor)

PATIENT: [NAME]               MRN: [MRN]
DOB: [DATE]                   DOS: [DATE]

CHIEF COMPLAINT: [AGE]-year-old male presents with chest pain x 2 days.

HISTORY OF PRESENT ILLNESS:
[NAME] is a [AGE]-year-old male who presents to the ED with complaints
of substernal chest pain that began 2 days ago. Patient states pain
radiates to his left arm. He was previously seen at [FACILITY]
on [DATE]. His wife [NAME] drove him to the ED today.

Patient lives at [ADDRESS]. He can be
reached at [PHONE]. His insurance ID is [INSURANCE_ID].

ASSESSMENT/PLAN:
1. Acute coronary syndrome - will admit to cardiology
2. Contact cardiologist [PROVIDER] at ext. [PHONE_EXT]

Medical Record Number (MRN) Detection

MRNs are critical identifiers that must be removed for de-identification. However, MRN formats vary by institution:

MRN Format Patterns

  • Numeric: 12345678
  • Prefixed: MRN-12345678, PT123456
  • Alphanumeric: A1B2C3D4
  • Formatted: 123-45-6789
  • With check digit: 123456780

Custom MRN Pattern Configuration

{
    "mrn_patterns": [
        {
            "name": "standard_numeric",
            "pattern": "\\b\\d{7,10}\\b",
            "context_required": ["MRN", "Medical Record", "Patient ID", "Chart"]
        },
        {
            "name": "prefixed",
            "pattern": "\\b(MRN|PT|PAT)[-:]?\\d{6,10}\\b",
            "context_required": false
        },
        {
            "name": "institution_specific",
            "pattern": "\\bABC-\\d{8}\\b",
            "context_required": false
        }
    ]
}

Date Handling Options

Dates in medical records require careful handling. We offer multiple approaches:

Full Redaction

Replace all dates with placeholder. Safest for de-identification but loses temporal information.

03/15/1965 → [DATE]

Year Only (Safe Harbor)

Keep year, remove month and day. Meets Safe Harbor requirements for most ages.

03/15/1965 → 1965

Date Shifting

Apply consistent offset to all dates. Preserves time intervals for longitudinal research.

03/15/1965 → 07/22/1965 (shifted +129 days)

Age Conversion

Convert birth date to age at time of service. For ages over 89, use "90+".

DOB: 03/15/1965 → Age: 58 years

Healthcare Data Format Support

We provide specialized processing for standard healthcare data formats:

HL7 v2 Message Processing

// Original HL7 v2 message
MSH|^~\&|HIS|Hospital|LAB|Lab|202401151430||ADT^A01|MSG001|P|2.4
PID|1|12345678|12345678^^^Hospital^MR||Smith^John^Q||19650315|M|||123 Oak St^^Springfield^IL^62701||5551234567

// Redacted HL7 message
MSH|^~\&|HIS|[FACILITY]|LAB|Lab|[DATETIME]||ADT^A01|[MSG_ID]|P|2.4
PID|1|[MRN]|[MRN]^^^[FACILITY]^MR||[NAME]||[DOB]|M|||[ADDRESS]||[PHONE]

FHIR Resource Processing

// Original FHIR Patient resource
{
    "resourceType": "Patient",
    "id": "patient-123",
    "identifier": [{
        "system": "http://hospital.org/mrn",
        "value": "12345678"
    }],
    "name": [{
        "family": "Smith",
        "given": ["John", "Q"]
    }],
    "birthDate": "1965-03-15",
    "address": [{
        "line": ["123 Oak Street"],
        "city": "Springfield",
        "state": "IL",
        "postalCode": "62701"
    }]
}

// De-identified FHIR resource
{
    "resourceType": "Patient",
    "id": "[RESOURCE_ID]",
    "identifier": [{
        "system": "[SYSTEM]",
        "value": "[MRN]"
    }],
    "name": [{
        "family": "[FAMILY_NAME]",
        "given": ["[GIVEN_NAME]"]
    }],
    "birthDate": "1965",
    "address": [{
        "line": ["[ADDRESS]"],
        "city": "[CITY]",
        "state": "IL",
        "postalCode": "627"
    }]
}

Medical NLP Capabilities

Our medical NLP understands clinical context to improve detection accuracy:

Clinical Context Understanding

  • Provider vs. Patient Names: "Dr. Johnson ordered labs" - provider name vs. "her son Johnson" - family PHI
  • Facility Names: "Transferred from Memorial Hospital" - may need redaction vs. drug names
  • Clinical Terms: "Patient is Johnson positive" - not a name vs. "Mr. Johnson arrived"
  • Date Context: "Take 2 tablets daily for 10 days" - not PHI vs. "seen on 01/15/2024"

API Integration

Medical Record Redaction Request

POST /v1/redact
{
    "document": "PATIENT: John Smith  MRN: 12345678...",
    "profile": "hipaa_safe_harbor",
    "options": {
        "date_handling": "year_only",
        "age_threshold": 89,
        "zip_code_handling": "three_digit",
        "preserve_providers": false,
        "preserve_facilities": false
    }
}

// Response
{
    "redacted_document": "PATIENT: [NAME]  MRN: [MRN]...",
    "compliance": {
        "method": "safe_harbor",
        "identifiers_found": {
            "names": 3,
            "mrn": 1,
            "dates": 4,
            "addresses": 1,
            "phone_numbers": 1,
            "insurance_ids": 1
        },
        "all_identifiers_redacted": true
    },
    "audit_log": "https://api.redactionapi.com/audit/abc123"
}

Use Cases

Clinical Research

De-identify patient records for retrospective studies, clinical trials, and outcomes research while preserving clinical value.

Healthcare Analytics

Prepare EHR data for population health analytics, quality metrics, and operational reporting without PHI exposure.

AI/ML Training

Create de-identified datasets for training clinical NLP models, diagnostic algorithms, and decision support systems.

Health Information Exchange

Enable safe data sharing between organizations, with research networks, or for public health reporting.

De-Identify Medical Records with Confidence

RedactionAPI provides healthcare organizations with comprehensive PHI detection and HIPAA-compliant de-identification. Process clinical documents at scale while meeting regulatory requirements.

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