Anonymization Service
Anonymization Service (ANS) helps protect sensitive patient information before claim data is used for analytics, review, or AI-assisted workflows — so downstream analysis works with the minimum PHI it needs.
Claim data is only useful if it can be handled safely.
Medical claims carry Protected Health Information at every layer — names, identifiers, dates, provider details. ANS is built on a simple principle: reduce exposure before analysis begins.
Raw records carry risk
Every copy of an unprotected medical record widens the privacy and compliance surface. Moving raw PHI between systems, teams, and tools multiplies the places where exposure can occur.
Consumer AI is not a safe channel
Pasting claim details into consumer AI tools can send PHI to systems with no healthcare data controls. ANS exists so teams get AI assistance without that shortcut.
Least exposure by design
Downstream screening and analysis rarely need direct identifiers to do their job. ANS applies a least-exposure principle: pass along what review requires, and minimize the rest.
Detect, de-identify, verify — every run.
ANS runs a consistent, repeatable sequence on incoming claim data, so protection is a workflow step rather than a manual habit.
Detect PHI
ANS scans claim documents and records for protected health information — names, identifiers, dates, contact details, and other sensitive fields.
Redact / de-identify
Detected fields are redacted or replaced with anonymized placeholders, according to the workflow's configuration and the needs of downstream review.
Verify & log
Output is checked before release to downstream modules, and each run is recorded so teams can show what was processed, when, and how.
Human control pointWhat minimization looks like on a record.
A simplified view of the same claim record before and after ANS processing. Direct identifiers are removed or replaced; the fields reviewers need stay intact.
Incoming claim record
Patient name: [Sample Patient]
Date of birth: [MM/DD/YYYY]
Member ID: [0000-SAMPLE]
Provider: [Sample Clinic Name]
Claim ID: CLM-4821 · Diagnosis and procedure codes
De-identified for review
Patient reference: ANON-4D2A
Date of birth: [REDACTED]
Member reference: ANON-9F17
Provider reference: ANON-77C3
Claim ID: CLM-4821 · Diagnosis and procedure codes retained
Illustrative example using fabricated data only.
Controls that fit compliance-minded teams.
ANS is one layer of ClaimClean's privacy posture. Deployment details, retention policies, and access controls are defined with each pilot.
ANS runs first, so everything after it works on protected data.
In the full ClaimClean workflow, ANS prepares claim data before any screening or analysis begins — the rest of the platform never needs raw identifiers to do its work.
Feeds Medical Fraud Filter
Protected claim data flows into Medical Fraud Filter (MF2) for screening against configurable policy and guideline checks, flagging which claims deserve deeper review.
Explore MF2Prepares Audit Insights Assist
Flagged claims move to Audit Insights Assist (AIA) for record analysis and evidence-ready reporting — with PHI already minimized before the analysis starts.
Explore AIAProtect the data before the analysis.
Request a security brief and we'll walk through PHI detection, de-identification, audit logging, and how ANS fits your review workflow.