# Building Patient Timelines

Assemble a chronological patient timeline from OpenMed-extracted clinical events...

- Repository: https://github.com/maziyarpanahi/openmed (5755 stars)
- Category: Writing
- License: Apache-2.0
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add maziyarpanahi/openmed --skill building-patient-timelines -g -a claude-code -y`
Codex: `npx skills add maziyarpanahi/openmed --skill building-patient-timelines -g -a codex -y`
Cursor: `npx skills add maziyarpanahi/openmed --skill building-patient-timelines -g -a cursor -y`
Gemini CLI: `npx skills add maziyarpanahi/openmed --skill building-patient-timelines -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add maziyarpanahi/openmed`

## Description

Assemble a chronological patient timeline from OpenMed-extracted clinical events, normalizing dates and resolving relative time expressions on-device. Use when the user wants to build a patient timeline, order events from clinical notes, reconstruct a longitudinal history, plot a course of illness, or turn analyze_text/deidentify output into a sorted sequence of dated encounters, diagnoses, medications, and procedures. Covers temporal normalization (absolute and relative), event modeling toward FHIR Encounter/Condition.onsetDateTime, anchoring to a document/admission date, and handling undated or ambiguous events. Consumes OpenMed analyze_text entities plus clinical temporality (resolving-clinical-context); produces a sorted event list ready for charting or FHIR export.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

```python
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

The rest: https://github.com/maziyarpanahi/openmed/blob/HEAD/skills/building-patient-timelines/SKILL.md