Building patient timelines

A patient timeline is a chronologically ordered list of clinical events, diagnoses, medications, procedures, encounters, each carrying a normalized date. OpenMed gives you the events (via analyze_text) and the clinical temporality of each mention (current vs. historical, see resolving-clinical-context); this skill turns those into a sorted timeline. Everything runs on-device, de-identify first if the source notes contain PHI, and keep raw identifiers out of logs.

When to use this skill

After you have extracted entities from one or more notes and want them ordered in time: a longitudinal history, a "course of illness" view, a feed for a summary card, or a pre-step before FHIR export. If you only need to extract entities, use extracting-clinical-entities. If you need negation/temporality on a single mention, use resolving-clinical-context.

Quick start

import datetime as dt
import openmed

note = (
    "Discharge summary, 2024-03-12. Patient admitted 2024-03-08 with chest pain. "
    "History of type 2 diabetes diagnosed in 2019. Started on metformin two days "
    "after admission. Cardiac catheterization performed yesterday."
)

# 1) Extract clinical events (entities carry char offsets: start/end)
result = openmed.analyze_text(note, output_format="dict")
events = result["entities"]   # each: {text, label, confidence, start, end}

# 2) Normalize the temporal frame: an explicit document/anchor date drives
#    resolution of relative expressions ("two days after", "yesterday").
anchor = dt.date(2024, 3, 12)  # parsed from the note header or document metadata

analyze_text returns {text, entities, model_name, timestamp, ...}; each entity is {text, label, confidence, start, end}. Use start/end to locate each event in the source and to find the nearest date expression.

Workflow