Understanding the output

This guide covers responses from POST /v1/medications/infer. The PDF endpoint (POST /v1/med-lists/from-documents) uses a related but different response shape — see Med lists from PDFs for its specific fields. Every inferred medication from /v1/medications/infer comes back with three fields you need to understand before acting on the data: status, confidence, and (when verbosity="full") an evidence trail.

`status` — what we think the medication’s state is

Status mirrors FHIR’s MedicationRequest.status vocabulary:

Status	Meaning
`active`	Patient is currently taking this medication
`completed`	Course finished (end date in past, or explicit `completed` status)
`stopped`	Explicitly stopped by a clinician
`cancelled`	Order was cancelled before dispensing
`unknown`	Not enough evidence to classify, or all data is too stale

See the full triggers table for the rules that produce each status.

`confidence` — how sure we are of the status

A float from 0.0 to 1.0. This is confidence in the status assignment, not probability the patient is taking the drug. It reflects data quality:

Multiple corroborating signals raise it (e.g., an active order plus a recent dispense for the same drug)
Missing dates lower it
Missing RxNorm codes lower it
Staleness is penalized — older signals carry less weight than recent ones

Use status AND confidence >= threshold together. A common pattern: show confidently-active meds prominently (confidence ≥ 0.80), show low-confidence meds in a secondary list with a “verify with patient” label.

`evidence` — which rules fired (full verbosity only)

When you request verbosity="full", each medication’s provenance entry includes the list of rules that contributed to the status. Examples:

Evidence tag	What it means
`recent_dispense`	A dispense was recorded recently
`active_order`	A `MedicationRequest` is `status=active` and was authored recently
`self_reported_active`	Patient-reported `MedicationStatement` with `reported=true`
`course_completed`	Explicit `completed` status or `effectivePeriod.end` in the past
`prescription_expired`	An old order with no recent fill activity to corroborate it
`order_stopped`	`MedicationRequest` has `status=stopped`
`order_cancelled`	`MedicationRequest` has `status=cancelled`
`stale_data`	All input data is too old to support a confident classification
`insufficient_signal`	No classifying rule fired (paired with `unknown` status)

The evidence list is the audit trail. If a clinician or user asks “why is this marked active?”, the answer is in provenance[med_id].evidence.

A worked example

Request:

{
  "verbosity": "full",
  "resources": [
    {
      "resourceType": "MedicationRequest",
      "id": "mr-1",
      "status": "active",
      "intent": "order",
      "authoredOn": "2026-03-05",
      "medicationCodeableConcept": { "coding": [{ "system": "http://www.nlm.nih.gov/research/umls/rxnorm", "code": "617312" }] }
    },
    {
      "resourceType": "MedicationDispense",
      "id": "md-1",
      "status": "completed",
      "whenHandedOver": "2026-04-09",
      "medicationCodeableConcept": { "coding": [{ "system": "http://www.nlm.nih.gov/research/umls/rxnorm", "code": "617312" }] }
    }
  ]
}

Response (trimmed):

{
  "medications": [
    {
      "id": "med_abc123",
      "display_name": "Atorvastatin 40 MG Oral Tablet",
      "status": "active",
      "confidence": 0.95,
      ...
    }
  ],
  "provenance": {
    "med_abc123": {
      "sources": ["MedicationRequest/mr-1", "MedicationDispense/md-1"],
      "evidence": ["active_order", "recent_dispense"],
      "enrichments": []
    }
  }
}

Two separate FHIR resources were deduplicated to one medication via RxNorm grouping. Two rules fired: active_order (the order is recent and active) and recent_dispense (a recent fill). The corroborating signals drive confidence high.

Acting on low-confidence results

Low confidence (< 0.7) usually means:

Only one signal fired (no corroboration)
The data is old
Key fields were missing (dates, RxNorm codes)

Don’t ignore low-confidence medications — they’re often the most interesting cases for clinical review. Surface them in a “needs verification” bucket rather than hiding them.

Getting started

FHIR

PDF documents

Guides

Resources

Understanding the output

`status` — what we think the medication’s state is

`confidence` — how sure we are of the status

`evidence` — which rules fired (full verbosity only)

A worked example

Acting on low-confidence results

​status — what we think the medication’s state is

​confidence — how sure we are of the status

​evidence — which rules fired (full verbosity only)

​A worked example

​Acting on low-confidence results

`status` — what we think the medication’s state is

`confidence` — how sure we are of the status

`evidence` — which rules fired (full verbosity only)

A worked example

Acting on low-confidence results