extracting-lab-tables

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"])