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MedListIQ accepts messy, under-coded FHIR and returns clean, standards-rich medication data. “Enrichment” means: we took a free-text value, a missing code, or an implicit concept — and added the explicit terminology codes that downstream systems need. Every enrichment is deterministic and rule-based. The full list of what we applied to a given medication is surfaced in provenance[med_id].enrichments when you request verbosity="full".

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Medication identity

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RxNorm canonicalization

Raw input often contains free-text drug names, NDC codes, branded names, or locally-coded medications. We map them to a single preferred RxNorm coding and stamp every medication with it.
  • System: http://www.nlm.nih.gov/research/umls/rxnorm
  • TTY preference: SCD (Semantic Clinical Drug) preferred over IN (Ingredient). When both are present, we pick the most specific.
  • Source: NIH RxNav API

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Deduplication

The same drug can appear as a MedicationRequest from the EHR, a MedicationDispense from the pharmacy, and a MedicationStatement reported by the patient. We collapse all three into one InferredMedication keyed by its RxNorm identity.

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Dose form inference

Tablet, capsule, patch, solution, suspension — inferred from the RxNorm SCD name when the input didn’t carry a discrete form field.

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Display name cleanup

SNOMED display strings ship with semantic tags like "Oral route (qualifier value)". We strip the parenthetical tags so UIs can render clean labels ("Oral route") without post-processing.

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Dosage semantics

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Routes

Free-text routes like "oral", "by mouth", or "PO" get mapped to both SNOMED CT and NCI Thesaurus codings.
  • SNOMED: http://snomed.info/sct — e.g., 26643006 (Oral route)
  • NCIT: http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl — e.g., C38288 (Oral)

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Administration methods

Method text ("inject", "infuse", "apply topically") gets mapped to SNOMED CT + NCIT.
  • SNOMED: method concepts under 129445006 (Administration — action)
  • NCIT: procedure concepts

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Frequency

Prescription-style frequency abbreviations and natural-language phrases are mapped into standard code systems:
  • FHIR v3 GTSAbbreviation: http://terminology.hl7.org/CodeSystem/v3-GTSAbbreviation — QD, BID, TID, QID, QHS, PRN, etc.
  • NCIT: matching frequency concepts for interop with NCI-coded systems

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Timing events

Textual timing anchors like "at bedtime", "in the morning", "with meals" are mapped to FHIR’s event-timing and v3 TimingEvent vocabularies:
  • FHIR event-timing: http://hl7.org/fhir/event-timing — MORN, AFT, EVE, NIGHT
  • v3 TimingEvent: http://terminology.hl7.org/CodeSystem/v3-TimingEvent — HS (bedtime), AC (before meals), PC (after meals)

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Duration extraction

Duration phrases in free-text sig strings ("for 7 days", "x 10 days") are parsed out and surfaced as structured duration objects with UCUM units.
  • UCUM: http://unitsofmeasure.org — d (day), wk (week), mo (month)

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Dose quantity + range

Numeric dose fields are surfaced with explicit UCUM units. Ranges like "1-2 tablets" are preserved as structured low/high rather than flattened.

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Clinical context

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Indications

reasonCode free-text and partial codings are enriched with both SNOMED CT and ICD-10-CM where mappings are available.
  • SNOMED: http://snomed.info/sct
  • ICD-10-CM: http://hl7.org/fhir/sid/icd-10-cm

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Prescription-label sig

A human-readable sig string is composed from the structured dosage fields — the kind of string a pharmacist or patient would see on a prescription label. Example:
"Take 1 tablet by mouth twice daily with meals for 10 days"
This is built from the enriched dosage components — route, frequency, timing events, duration — not from the original free-text patientInstruction. If the input was fully structured with no sig text, we still produce one.

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Status + confidence

Each medication is stamped with a status (active, completed, stopped, cancelled, unknown) and a confidence score. This is inference, not enrichment of source fields — see the Understanding the output guide for the signal logic and confidence math.

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What gets reported

When you request verbosity="full", every enrichment we applied appears in the provenance block: 
{
  "provenance": {
    "med_abc123": {
      "sources": ["MedicationRequest/mr-1", "MedicationDispense/md-1"],
      "evidence": ["active_order", "recent_dispense"],
      "enrichments": [
        "rxnorm_canonicalized",
        "route_snomed_added",
        "route_ncit_added",
        "frequency_coded",
        "dose_form_inferred",
        "sig_composed"
      ]
    }
  }
}
This is the audit trail. Every terminology code in the response is accounted for — either present in the input, or added by an enrichment with a named tag.