/extracting-lab-tables

name: extracting-lab-tables
description: "Detects and extracts tabular laboratory panels from PDFs, scans, and images into structured rows ready for OpenMed and FHIR. Use when the user has a CBC, CMP, lipid panel, or other lab report as a scanned image / PDF / spreadsheet and needs the test name, value, unit, reference range, and abnormal flag as clean rows. Trigger keywords: lab table extraction, lab panel, OCR labs, table detection, layout analysis, header detection, reference range column, abnormal flag column, LOINC, UCUM, CBC, CMP, structured labs. Pairs before OpenMed: OCR/parse the table on-device (openmed.multimodal.ocr.ocr, read_table), de-identify embedded PHI with openmed.deidentify, then hand structured rows to LOINC/UCUM mapping and openmed.clinical lab flagging. Image/CSV/TSV intake is supported; PDF/DOCX raise UnsupportedDocumentError — render those to images or text first."
license: Apache-2.0
metadata:
  project: OpenMed
  category: imaging-ocr
  pairs: before
  version: "1.0"

Extracting lab tables from documents and scans

Lab results arrive as **tables**: a column of test names, a value column, units, a reference range, and an abnormal flag (H/L/Crit). To use them downstream you must recover that grid from a PDF, scan, or spreadsheet into clean rows — then code each test to **LOINC**, normalize units with **UCUM**, and flag abnormals.

This skill is the **intake** step: it OCRs/parses the table on-device with `openmed.multimodal`, de-identifies any embedded PHI, and emits structured rows. It pairs **before** OpenMed's clinical helpers — the LOINC/UCUM coding and the high/low/critical flag are downstream (see `parsing-lab-values`).

When to use

• You have a lab report as a **scanned image / photo / PDF page** and need the

panel as rows, not pixels.

• The source is a **CSV/TSV** export and you need columns classified (which is

the value, the unit, the range, the flag) and PHI columns redacted.

• You need machine-readable rows to feed LOINC mapping and a FHIR

`Observation`/`DiagnosticReport`.

What OpenMed gives you here

`openmed.multimodal` ships the intake primitives (no heavy deps at import; the OCR backend loads lazily):

• `openmed.multimodal.ocr.ocr(image, engine=...)` → an `OcrResult` whose `.words`

are `OcrWord(text, bbox, confidence, page)` and `.text` is the joined string. `OcrResult.to_document()` bridges each word (with its pixel bbox) into an `ExtractedDocument` so detected PHI can project back to the source location.

• `read_table(...)` → a `TableView` (`headers`, `rows`, `delimiter`,

`has_header`, `columns`) for delimited text; `classify_columns(...)` labels each column; `redact_table(...)` → a `RedactedTable` with a PHI-safe `manifest`.

Engines: Tesseract (`pip install "openmed[multimodal]"` + the system binary) or PaddleOCR (`pip install "openmed[ocr-paddle]"`). `ocr()` auto-selects the first installed backend.

Quick start

from openmed.multimodal.ocr import ocr
from openmed.multimodal import read_table, classify_columns, redact_table

# A) Scanned / image lab report -> words with pixel boxes.
result = ocr("cbc_report.png")               # OcrResult
for w in result.words[:5]:
    print(repr(w.text), w.bbox, round(w.confidence, 2), "p", w.page)

doc = result.to_document()                   # ExtractedDocument; bbox preserved

# B) Delimited lab export (CSV/TSV) -> classified, PHI-redacted rows.
csv_text = (
    "PatientName,Test,Value,Unit,RefRange,Flag\n"
    "Jane Roe,Hemoglobin,9.1,g/dL,12.0-15.5,L\n"
    "Jane Roe,Glucose,148,mg/dL,70-99,H\n"
)
view = read_table(csv_text)                  # TableView
view = classify_columns(view)                # tag PHI vs data columns
redacted = redact_table(view)                # RedactedTable: PatientName redacted

for row in redacted.rows:
    print(row)                               # name column masked; lab data intact
for col in redacted.manifest:                # PHI-safe per-column audit manifest
    print(col["column_name"], col["assigned_class"], col["action"])

For an OCR'd (image) table, you reconstruct the grid yourself from word boxes (next section) — OCR yields positioned words, not a delimited table.

Workflow

1. **Detect the source type.** CSV/TSV → `read_table`. Image/scan → `ocr()`. PDF/DOCX are **not** directly parseable (they raise `UnsupportedDocumentError`); render PDF pages to images first, or extract their text layer, then OCR. 2. **OCR with positions.** `ocr()` returns `OcrWord`s carrying `bbox` and `page`. Keep the boxes — they let you cluster words into rows/columns and project PHI redaction back to pixels. 3. **Reconstruct the grid.** Cluster words by their `bbox` *y* into rows, by *x* into columns. The header row names the columns; align body cells to those x bands. Confidence (`OcrWord.confidence`) flags shaky cells for review. 4. **Identify the lab columns.** Map headers to roles: *test name*, *value*, *unit*, *reference range*, *flag*. For delimited input, `classify_columns` tags PHI columns (name/MRN/DOB) so `redact_table` masks them. 5. **De-identify embedded PHI.** Patient name/MRN often sit in the table header or a leading column. Redact those columns (`redact_table`) and run free-text cells through `openmed.deidentify` before the rows leave the device. 6. **Emit structured rows** `{test, value, unit, ref_range, flag}` per result and hand off to LOINC/UCUM coding and `parsing-lab-values`.

Hand-off to / from OpenMed

• **To** `parsing-lab-values` (`openmed.clinical.parse_reference_range`,

`derive_abnormal_flag`): pass the parsed value + `ref_range` (+ any explicit lab flag) to get a structured low/normal/high/critical signal.

• **To** `mapping-loinc`: code each test name to a LOINC code; normalize the unit

with UCUM. OpenMed emits the row; the terminology binding is out-of-process.

• **To** FHIR (`exporting-to-fhir`): each row becomes an `Observation`

(`code`=LOINC, `valueQuantity