Generating synthetic surrogates

method="replace" swaps each detected identifier for a realistic, type-matched fake — John Doe becomes Mark Lee, a phone becomes a plausible phone, a date becomes a plausible date. Unlike opaque [REDACTED]/[NAME] masks, surrogate text reads naturally and stays parseable by downstream NLP, while still containing no real PHI. OpenMed generates surrogates on-device via Faker-backed providers keyed to each canonical label.

When to use this skill

Use surrogates when the de-identified text must remain readable or machine- parseable: training data for clinical NLP, demos, QA, or notes a human still needs to skim. If you only need the identifiers gone and don't care about readability, plain method="mask" is simpler and more obviously redacted.

Quick start

import openmed

note = (
    "Patient John Doe (MRN 1234567) saw Dr. John Doe's colleague on 2024-03-02. "
    "Reach John Doe at 617-555-0142."
)

result = openmed.deidentify(
    note,
    method="replace",
    consistent=True,     # every "John Doe" -> the SAME surrogate within this call
    seed=42,             # reproducible across runs
    locale="en_US",      # shapes the fakes; defaults from lang via LANG_TO_LOCALE
)
print(result.deidentified_text)
# Patient Mark Lee (MRN 8830127) saw Dr. Mark Lee's colleague on 2024-07-18. ...

consistent=True is what makes the output coherent: the three mentions of "John Doe" collapse to one fake identity instead of three different ones, so the note still makes sense. seed= makes that mapping reproducible run to run.

Surrogates vs opaque redaction

	method="mask" ([NAME])	method="replace" (surrogate)
Readability	low — placeholders	high — reads like a real note
Downstream NLP	tokenizers see [NAME] everywhere	natural distribution preserved
Co-reference	lost (all [NAME])	preserved with consistent=True
Obvious it's de-identified	yes	no (must be tracked out-of-band)
Reversible	with keep_mapping=True	with keep_mapping=True

Custom providers and label generators

When a built-in surrogate doesn't match your house format (e.g. your MRNs are H + 7 digits), register a generator or a Faker provider.

from openmed import (
    register_label_generator, register_clinical_provider,
    Anonymizer, AnonymizerConfig,
)

# Override the surrogate for one canonical label. Signature: (faker, original, *, locale)
def hospital_mrn(faker, original, *, locale):
    return f"H{faker.numerify('#######')}"

register_label_generator("ID_NUM", hospital_mrn)   # global, all new Anonymizers

# Add a whole custom Faker provider (e.g. proprietary identifier formats):
register_clinical_provider(MyClinicalProvider)     # a faker BaseProvider subclass

# Per-instance control (preferred for isolation): pass providers via config,
# and pull a single surrogate directly when you need one.
anon = Anonymizer(AnonymizerConfig(
    lang="en", consistent=True, seed=7, custom_providers=[MyClinicalProvider],
))
fake = anon.surrogate("1234567", "ID_NUM")

Use register_label_generator(canonical_label, fn) to swap one label's surrogate; register_clinical_provider(provider) to add providers globally; or AnonymizerConfig.custom_providers for per-run scoping. Validate any custom label against openmed.CANONICAL_LABELS.

Workflow

1. Choose method="replace" (or a profile like gdpr_pseudonymization / canada_pipeda that replaces by default — see configuring-privacy-policies).
2. Enable consistency with consistent=True and a seed= so repeated mentions resolve to one identity and the result is reproducible.
3. Set locale= so surrogates look native (pt_BR, de_DE, …); it defaults from lang via LANG_TO_LOCALE (deidentifying-multilingual-text).
4. Register custom generators for any house-specific formats (MRN, account, address) before the run.
5. If reversibility is needed, add keep_mapping=True and store result.mapping as a secret, separate from the output.
6. Verify no surrogate collides with a real value and residual risk is low (auditing-deidentification-runs).

Hand-off to / from OpenMed

• Core de-id: deidentifying-clinical-text — method, thresholds, keep_mapping, policies.
• Policies that replace: configuring-privacy-policies (gdpr_pseudonymization, canada_pipeda).
• Multilingual surrogates: deidentifying-multilingual-text (lang/locale).
• Restore: openmed.reidentify(text, mapping) when keep_mapping=True.
• Other surfaces: MCP openmed_deidentify / REST POST /pii/deidentify.

Edge cases & gotchas

• Surrogates must not collide with real values. A fake MRN that happens to be a real patient's MRN re-identifies them. Keep generated identifiers out of the real ID space (dedicated prefix/range) and check against your live keys.
• Surrogates look real but are not labeled. Anyone reading the output cannot tell it's de-identified. Track provenance out-of-band (e.g. an AuditReport) so surrogate notes are never mistaken for source records.
• Keep the mapping secret. With keep_mapping=True, result.mapping re-identifies everyone — encrypt it and store it apart from the output.
• register_label_generator is global and process-wide. It mutates a shared registry; for isolation use AnonymizerConfig.custom_providers instead.
• Consistency is per-document by default. consistent=True makes mentions agree within a call; cross-document stability requires the same seed.
• Permissive licensing only. Don't build providers from UMLS/SNOMED/CPT/MIMIC/i2b2/n2c2; call restricted resources out-of-process.

Standards & references

• GDPR pseudonymization, Regulation (EU) 2016/679 Art. 4(5): https://eur-lex.europa.eu/eli/reg/2016/679/oj
• HIPAA de-identification, 45 CFR 164.514(b): https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identification/index.html
• OpenMed source: openmed/core/pii.py (deidentify(method="replace")), openmed/core/anonymizer/ (Anonymizer, AnonymizerConfig, register_label_generator, register_clinical_provider, LANG_TO_LOCALE).