Building Patient Timelines

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

1. De-identify if needed. If notes carry PHI, run openmed.deidentify(...) first, or keep the timeline keyed by stable internal IDs — never log raw names/MRNs.
2. Extract events. openmed.analyze_text(note) for conditions, drugs, procedures; pick the model that matches your target entities (choosing-openmed-models).
3. Resolve temporality. For each event, use resolving-clinical-context to tag it current / historical / hypothetical and to drop negated or family-history mentions that should not appear on the patient's own line.
4. Normalize dates. Map each event to a date:
   • Absolute (2024-03-08, March 2019) → parse directly. Record the granularity (day / month / year) — a year-only event sorts to a coarse bucket, not a fake Jan 1.
   • Relative (two days after admission, yesterday, on POD 2) → resolve against an anchor: the document date, admission date, or a prior event's date. Without an anchor, relative expressions are unresolvable — flag them, don't guess.
5. Build event records. One record per event: (date, granularity, label, surface_text, char_span, temporality, confidence, source_note_id).
6. Sort and de-duplicate. Sort by (date, granularity); merge repeated mentions of the same event across notes (same label + overlapping date).
7. Emit. A sorted list for a UI, or FHIR resources (see hand-off).

Worked example: events → sorted timeline

def to_timeline(events, *, anchor, note_id):
    """events: list of {text,label,start,end,confidence}. anchor: date.
    Returns sorted [(date, granularity, label, text, confidence)]."""
    timeline = []
    for e in events:
        date, gran = resolve_event_date(e, note=note, anchor=anchor)  # your resolver
        if date is None:
            continue  # undated/unresolvable: route to an "undated" bucket, don't drop silently
        timeline.append((date, gran, e["label"], e["text"], e["confidence"]))
    # year-only ('Y') sorts before month ('M') before day ('D') on ties
    order = {"Y": 0, "M": 1, "D": 2}
    return sorted(timeline, key=lambda r: (r[0], order[r[1]]))

# resolve_event_date handles: ISO dates, "March 2019" (gran='M'),
# "yesterday"/"two days after admission" (relative to anchor/admission), POD-n, etc.

Hand-off to / from OpenMed

• From OpenMed: analyze_text entities (extracting-clinical-entities) and clinical context tags (resolving-clinical-context) are the inputs. Run deidentify upstream when notes carry PHI.
• To OpenMed / interop: feed the sorted, dated events into exporting-to-fhir (openmed.interop). Map an admission/discharge event to a FHIR Encounter, a diagnosis date to Condition.onsetDateTime, a med-start to MedicationStatement.effectiveDateTime, a procedure to Procedure.performedDateTime.
• Downstream: the same timeline feeds etl-to-omop-cdm (start/end dates on condition_occurrence / drug_exposure) and clinical-summary cards.

Edge cases & gotchas

• No anchor → no relative dates. "Two days later", "POD 2", "yesterday" are meaningless without a reference date. Parse the document date / admission date first; if absent, keep the event in an undated bucket rather than inventing a date.
• Preserve granularity. Don't coerce "2019" to 2019-01-01 and then sort it as if it were a precise day — it'll outrank real January events. Carry a granularity flag and sort coarse dates conservatively.
• Drop the wrong people and tenses. Negated ("no prior MI"), hypothetical ("would consider surgery if…"), and family-history mentions must not land on the patient's timeline. That's what the temporality pass is for.
• Time zones and 2-digit years are ambiguous — normalize to dates (not datetimes) for clinical timelines unless you genuinely have timestamps, and resolve dd/mm vs mm/dd from the document locale, not a guess.
• Future/scheduled events (follow-up appointments) are real but belong on a separate "planned" lane, not interleaved with what already happened.
• No raw PHI in logs. Log timeline events by label + offset + note id, never the patient's name or the raw note text.

Standards & references

• FHIR R4 Encounter: https://www.hl7.org/fhir/encounter.html
• FHIR R4 Condition (onsetDateTime, recordedDate): https://www.hl7.org/fhir/condition.html
• ISO 8601 date/time representation: https://www.iso.org/iso-8601-date-and-time-format.html
• Background on clinical temporal expression normalization (TimeML / i2b2 2012 temporal relations task): https://www.i2b2.org/NLP/TemporalRelations/
• OpenMed source: openmed/processing/ (analyze_text output), openmed.clinical (temporality), openmed.interop (FHIR export).