Alpha Quantum ALPHA QUANTUM
Try Demos
Home
Platforms
Website Categorization API AI Tools Blocklist AI Agent Allowlist URL Categorization Database Product Categorization API PII Detection & Anonymization Resume Reader API
Solutions
Brand Safety Content Moderation Customer Intelligence Data Privacy Digital Marketing Document Intelligence E-Commerce Financial Services Healthcare Legal & Compliance
Industries
Telecommunications Healthcare Finance Retail Government Manufacturing Media Insurance Energy Education
About Contact
Try Demos
Healthcare Data Protection

De-identify clinical data with AI-powered PHI detection at scale

Detect and redact protected health information across clinical notes, medical images, audio recordings and EHR exports. 99.9% detection accuracy across 70+ PHI entity types in 50+ languages. HIPAA and GDPR compliant from ingestion to output delivery.

Contact Us Anonymization API

Anonymization API

PHI de-identification engine

Detect, classify and redact protected health information from unstructured clinical text, scanned documents, medical images and audio recordings in real time.

PII Types
70+
Accuracy
99.9%
Languages
50+
Compliance
HIPAA/GDPR
Explore platform
The Challenge

Healthcare data carries the highest regulatory burden in any industry

Protected health information appears in every document a hospital generates. Clinical notes, radiology reports, discharge summaries, insurance claims and research datasets all contain identifiers that HIPAA requires to be removed before sharing, analysis or secondary use.

PHI embedded in unstructured clinical notes

Physicians dictate patient encounters as free-text narratives. Patient names, medical record numbers, dates of birth, diagnoses and medication dosages appear throughout progress notes, operative reports and consultation letters. Manual redaction is slow, expensive and error-prone. The Anonymization API detects 70+ PHI entity types with 99.9% accuracy, processing thousands of clinical documents per minute without human review bottlenecks.

Medical images contain burned-in patient identifiers

DICOM files, radiology scans, pathology slides and clinical photographs frequently carry patient names, MRNs and dates of service embedded directly in pixel data. DICOM header stripping alone is insufficient because burned-in annotations survive metadata removal. The API applies computer vision models to detect and redact text overlays, patient faces and identifying marks from medical images automatically.

Multi-language patient records challenge traditional NLP

Health systems serving diverse populations generate records in multiple languages. Discharge instructions in Spanish, consent forms in Mandarin, referral letters in Arabic. Rule-based redaction systems trained on English fail silently on non-English text. The Anonymization API supports 50+ languages with context-aware NLP models that recognize PHI patterns regardless of script or grammar structure.

Research data sharing stalls without automated de-identification

Clinical trials, population health studies and multi-site research collaborations require sharing patient data across institutional boundaries. HIPAA Safe Harbor demands removal of 18 identifier categories. Expert determination requires statistical verification. Both pathways are manual, slow and difficult to audit. Automated PHI detection with configurable anonymization techniques accelerates research while maintaining full compliance documentation.

PHI Entity Detection

70+ protected health information entity types detected automatically

The Anonymization API identifies and classifies every PHI element defined under HIPAA, plus additional clinical identifiers specific to healthcare workflows. Each entity is detected with contextual awareness, distinguishing patient names from provider names and medication names from diagnosis codes.

PHI EntityHIPAA CategoryExampleDetection MethodAccuracy
Patient NamesDirect IdentifierJohn M. RobertsNER + context model99.9%
Medical Record NumbersDirect IdentifierMRN-2847391Pattern + prefix matching99.8%
Dates of BirthQuasi-Identifier03/15/1987Format + context analysis99.7%
Diagnoses & ICD CodesClinical DataType 2 Diabetes (E11.9)Medical NER + code lookup99.6%
Medications & DosagesClinical DataMetformin 500mg BIDDrug dictionary + NER99.5%
Insurance IDsDirect IdentifierBCBS-FL-9928471Pattern + payer prefix DB99.8%
Provider NamesIndirect IdentifierDr. Sarah Chen, MDTitle + credential detection99.7%
Facility NamesIndirect IdentifierMemorial Regional HospitalFacility registry + NER99.4%
Dates of ServiceQuasi-IdentifierAdmitted 2025-09-12Temporal context analysis99.6%
Lab Results & VitalsClinical DataHbA1c: 7.2%, BP 138/82Value + unit + range parsing99.3%
Full entity type reference on anonymizationapi.com. All entity types available via real-time API. Custom entity types supported for organization-specific identifiers.
Clinical NotePatient Name · MRN · DOB · Insurance ID · Provider
Radiology ReportFacility · Accession # · Referring MD · Study Date
Discharge SummaryDiagnosis · Medications · Follow-up Dates · PCP Name
Insurance ClaimSubscriber ID · Group # · Claim Amount · Service Date
Industry Segments

Purpose-built de-identification for every healthcare segment

Hospitals, pharmaceutical companies, insurance carriers and research institutions each handle PHI differently. The Anonymization API adapts its detection models and anonymization techniques to the specific document types, compliance requirements and workflows of each segment.

Hospitals
Pharma
Insurance
Research

Clinical Documentation De-identification

Process progress notes, operative reports, consultation letters and discharge summaries at scale. The API handles unstructured physician narratives, abbreviations, medical terminology and multi-provider documentation chains. Integrate directly with Epic, Cerner and MEDITECH EHR systems via HL7 FHIR or custom connectors to de-identify records before they leave the production environment.

Medical Image Redaction

Remove burned-in patient identifiers from DICOM files, ultrasound captures, endoscopy images and clinical photographs. Computer vision models detect text overlays, patient faces and identifying marks that survive DICOM header stripping. Process thousands of images per hour for teaching file creation, quality review sharing and multi-site collaboration without exposing PHI.

Quality Reporting and Analytics

De-identify patient records for population health analytics, readmission analysis and quality improvement initiatives. Pseudonymization preserves longitudinal linkability so the same patient maps to the same pseudonym across encounters, enabling trend analysis without re-identification risk. Generate HIPAA-compliant datasets for value-based care reporting and CMS quality measure submission.

Clinical Trial Data Anonymization

De-identify case report forms, adverse event narratives, investigator brochures and regulatory submissions before sharing with CROs, regulatory agencies and publication committees. The API handles multi-site trial data where patient identifiers differ across institutions. Configurable anonymization techniques support both HIPAA Safe Harbor and ICH E6 requirements for global trials.

Real-World Evidence Processing

Process electronic health records, claims databases and patient registries for real-world evidence studies. The API de-identifies structured and unstructured data while preserving clinical relationships required for outcome analysis. Support pharmacovigilance signal detection, drug utilization studies and health economics research with privacy-compliant datasets.

Regulatory Submission Redaction

Prepare clinical study reports, NDA packages and FDA briefing documents with automated redaction of patient identifiers, investigator names and site-specific information. The API processes PDF, DOCX and PPTX files while maintaining document formatting, pagination and cross-reference integrity required for regulatory review workflows.

Claims Processing De-identification

Redact subscriber IDs, group numbers, SSNs and provider NPIs from claims data shared with actuaries, auditors and reinsurers. The API processes CMS-1500, UB-04 and 837 transaction formats. Format-preserving pseudonymization maintains referential integrity across claim lines so downstream analytics operate on consistent identifiers without exposing real patient data.

Member Communication Redaction

De-identify explanation of benefits documents, prior authorization letters and appeals correspondence before use in training datasets, quality audits and process improvement analysis. The API detects member names, policy numbers, diagnosis codes and provider information across structured forms and free-text correspondence simultaneously.

Fraud Investigation Support

Prepare investigation files for sharing across departments and with external agencies. Redact patient PHI while preserving the billing patterns, procedure sequences and provider relationships needed for fraud detection analysis. The API supports selective redaction where investigator-relevant fields remain visible while patient identifiers are masked according to minimum necessary standards.

Multi-Site Research Collaboration

Enable data sharing across academic medical centers, health systems and international research consortia. The API applies consistent de-identification rules across institutions where identifier formats, EHR systems and documentation standards differ. Pseudonymization with site-specific keys allows each institution to re-link records internally while preventing cross-site re-identification.

Biobank and Genomic Data

De-identify clinical phenotype data linked to biobank specimens and genomic datasets. The API handles the unique re-identification risks of genomic data by removing direct identifiers from accompanying clinical records while flagging quasi-identifiers that could enable re-identification when combined with publicly available genetic databases.

IRB and Ethics Compliance

Generate de-identification audit logs that satisfy Institutional Review Board requirements for data use agreements. The API documents every detected entity, the anonymization technique applied and the confidence score for each transformation. These audit trails serve as compliance evidence for IRB renewals, data use committee reviews and external audits.

Full healthcare solutions documentation
Core Capabilities

Six engines powering healthcare data protection

The Anonymization API combines natural language processing, computer vision, speech recognition and compliance rule engines into a single platform. Each capability operates independently or in concert across multi-modal clinical data pipelines.

Clinical Note De-identification

Process unstructured physician narratives, progress notes, operative reports, consultation letters and discharge summaries. Context-aware NLP models distinguish between patient names and medication names, facility references and geographic locations, diagnosis descriptions and procedure codes. Handles medical abbreviations, misspellings and multi-provider documentation chains across 50+ languages.

Text de-identification

Medical Image Redaction

Computer vision models detect and redact burned-in text overlays, patient faces, identifying marks and screen captures from DICOM files, ultrasound images, pathology slides and clinical photographs. DICOM header stripping removes metadata identifiers while pixel-level analysis catches burned-in annotations that metadata removal misses. Batch processing supports thousands of images per hour.

Image redaction

Audio and Speech Redaction

Transcribe and redact physician dictations, patient intake recordings, telehealth sessions and clinical trial interviews. Speech-to-text conversion followed by PHI detection identifies patient names, dates, locations and medical record numbers in spoken audio. Redacted segments are replaced with silence or synthetic speech while preserving the clinical content of the recording.

Audio processing

HIPAA Compliance Engine

Built-in rule sets for HIPAA Safe Harbor (18 identifier categories), HIPAA Expert Determination and GDPR Article 9 special category processing. The compliance engine validates that every required identifier type has been detected and appropriately transformed before output delivery. Generates audit-ready compliance reports documenting detection coverage, transformation methods and residual risk scores for each processed document.

Pseudonymization for Research

Replace real identifiers with consistent synthetic values that preserve analytical utility. The same patient always maps to the same pseudonym across documents and encounters, enabling longitudinal research without re-identification risk. Format-preserving pseudonymization generates realistic replacements that match the structure of the original data, maintaining referential integrity across linked datasets and database joins.

Format-Preserving Encryption

Encrypt identifiers while maintaining their original format and length. Social Security numbers remain nine digits, dates stay in date format, phone numbers keep their structure. Downstream systems that validate field formats continue to function without modification. Authorized users with decryption keys can reverse the transformation when re-identification is legally permitted, combining the security of encryption with the practicality of format preservation.

Use Cases

How healthcare organizations use the Anonymization API

From clinical trial data sharing to insurance claims processing, the same PHI detection engine adapts to the document types, compliance requirements and operational workflows of different healthcare use cases.

01

Clinical Trial Data Sharing

De-identify case report forms, adverse event narratives and patient diaries before sharing with contract research organizations, data monitoring committees and regulatory agencies. Process multi-site trial data where identifier formats differ across institutions. Generate HIPAA-compliant and ICH E6-compliant datasets that satisfy both domestic and international regulatory requirements for clinical research submissions.

02

Patient Intake Automation

Redact PHI from scanned intake forms, insurance cards, photo IDs and handwritten questionnaires during the patient registration process. OCR-powered detection identifies handwritten names, dates and insurance numbers on paper forms. Feed de-identified intake data into analytics pipelines that measure registration efficiency, demographic trends and insurance coverage patterns without exposing individual patient information.

03

Telehealth Transcript Redaction

Process telehealth video and audio recordings to generate de-identified transcripts for quality assurance review, clinician training and outcomes research. The API transcribes spoken audio, detects PHI in the transcript and redacts corresponding audio segments. Teaching hospitals use de-identified telehealth sessions for resident education without requiring individual patient consent for each viewing.

04

EHR Migration and Testing

Create de-identified copies of production EHR databases for system migration testing, vendor evaluation and software development. Pseudonymization preserves referential integrity across patient encounters, orders, results and billing records so migration testing validates real data relationships. Development teams work with realistic datasets without access to actual patient health information throughout the testing lifecycle.

05

Medical Billing Analytics

De-identify billing records, charge capture data and revenue cycle reports for operational analytics, benchmarking and consultant sharing. The API redacts patient and subscriber identifiers while preserving CPT codes, DRG assignments, charge amounts and payer classifications needed for financial analysis. Share revenue cycle performance data with external consultants without creating HIPAA-reportable disclosures.

06

Insurance Claims Processing

Redact PHI from claims data shared with actuaries, auditors, reinsurance carriers and fraud investigation units. Process CMS-1500 forms, UB-04 claims, electronic 837 transactions and explanation of benefits documents. Format-preserving pseudonymization maintains claim line referential integrity so downstream actuarial models and fraud detection algorithms operate on consistent identifiers across the claim lifecycle.

Processing Pipeline

From raw clinical data to HIPAA-compliant output

Four processing stages transform clinical documents, images and audio into de-identified outputs with full audit trails. Real-time API for individual documents. Batch mode for bulk processing across entire EHR exports.

01
Ingest
Clinical notes, DICOM images, PDF reports, audio files via API or batch upload
02
Detect
70+ PHI entity types identified with NER, pattern matching and computer vision
03
Transform
Redaction, pseudonymization, generalization or format-preserving encryption applied
04
Deliver
De-identified output with compliance audit log and entity detection report
Platforms

Three platforms powering healthcare data protection

The Anonymization API handles PHI detection and de-identification. The Website Categorization API provides COPPA compliance scoring for child-directed health content. The Resume Reader API accelerates clinical staff recruitment with automated credential extraction from 114 structured fields.

Anonymization API Website Categorization API Resume Reader API
Scale

Enterprise-grade PHI de-identification infrastructure

Production-hardened across healthcare systems, pharmaceutical companies, insurance carriers and research institutions. Real clinical data volumes, real compliance requirements, real audit trails.

70+
PHI Entity Types
99.9%
Detection Accuracy
50+
Languages Supported
10M+
Records Protected
<100ms
API Response Time
HIPAA
Fully Compliant
300+
Organizations Trust Us
99.9%
Uptime SLA

Test PHI de-identification on your clinical data

Send us a sample of clinical documents, medical images or audio recordings. We will return de-identified versions with full entity detection reports and compliance documentation so you can evaluate accuracy, coverage and integration requirements before deployment.

Contact Us Anonymization API