How Standardization Works

Health data arrives messy — “血糖(空腹)”, “FBG”, “Glucose, fasting” all name the same indicator, and the units vary just as much. Mirobody standardizes every structured reading written through POST /v1/data or stored by POST /v1/standardize, so the agent and your queries see one coherent, coded dataset.

POST /v1/files is a separate storage and text-extraction surface; uploading a file standardizes its report values into structured readings automatically. /v1/standardize is the explicit path for that same extraction — use it to inspect the result (dry-run), or to standardize text / file_key sources on demand.

The pipeline

Both a document and a structured record enter the same pipeline:

1. 1
   Read the source PDF / image → text (OCR); Excel → rows.
2. 2
   Pull out readings Find each {indicator, value, unit, date}. Documents only — a /v1/data record is already structured and skips straight to coding.
3. 3
   Code the indicator Match the name to a LOINC code. With no confident match, loinc_code stays null — never a wrong code.
4. 4
   Normalize the unit Fold the unit spelling to one UCUM form and parse the value to a number: "mg/dl" → mg/dL.
5. 5
   Mirror to FHIR One FHIR R4 Observation per reading, addressable by fhir_resource_id.

The important part is step 3: codes are matched, never invented. A model is great at reading a document but shouldn’t be trusted to recite a code system — a wrong LOINC code is worse than none. So when the match isn’t confident, Mirobody keeps the raw name and leaves loinc_code null rather than guess.

Before / after

What you send to /v1/data (or what /v1/standardize reads from a report) vs. what the structured store holds:

	Before (as written)	After (standardized)
Indicator	"血糖(空腹)" / "FBG" / "Glucose, fasting"	loinc_code: "1558-6", canonical_name: "Fasting glucose [Mass/volume] in Serum or Plasma"
Value	"97 mg/dL" (one string)	value: "97" (raw value, no unit), parsed_value: "97", parsed_unit: "mg/dL" (UCUM)
Unit spelling	"mg/dl", "MG/DL", "mg/dL"	"mg/dL" (one UCUM form)
Interop	free text	FHIR R4 Observation (fhir_resource_id)
Original	—	kept: value holds the raw value as written (e.g. 97, no unit appended — the unit rides in parsed_unit); raw names/units preserved

Three spellings of the same test become one series — trend queries, the agent’s data tools, and your own analytics all see it as one indicator.

Where you see it

Surface	What appears
POST /v1/data	Response counts standardized alongside ingested.
GET /v1/data	Every row carries parsed_value / parsed_unit / loinc_code / canonical_name / fhir_resource_id.
POST /v1/standardize	The whole pipeline as a synchronous call — dry-run by default, so you can inspect standardization before committing a write.
The agent	Grounded answers query the standardized series — which is why “how’s my glucose?” finds records written as “FBG”.

Cookbook: one call, report → structured data

# Inspect first (nothing persisted) …
curl https://api.mirobody.ai/v1/standardize \
  -H "Authorization: Bearer $MIROBODY_API_KEY" \
  -F "user=alice" -F "file=@lab_report.pdf"

# … then commit the same extraction
curl https://api.mirobody.ai/v1/standardize \
  -H "Authorization: Bearer $MIROBODY_API_KEY" \
  -F "user=alice" -F "file=@lab_report.pdf" \
  -F "store=true" -F "retention=permanent"

See Standardize a report for the full row shape (indicator_raw, loinc_code, confidence, …).