SynthIPS — Synthetic IPS NER Validation Dataset

A validation dataset of 10 synthetic patients across 29 clinical encounters, annotated for the three core entity types of the International Patient Summary (IPS) standard: Condition, Medication, and Lab.

The International Patient Summary (IPS) is an HL7 FHIR standard for a minimal, specialty-agnostic health record designed for cross-border and cross-system care continuity. SynthIPS is built around its three primary clinical sections — problems, medications, and lab results — and provides realistic clinical documents with ground-truth annotations aligned to IPS entity types, ICD-10 condition codes, ATC medication codes, and LOINC lab identifiers.

Documents include structured blocks (problem lists, medication tables, lab grids) alongside free-text narrative sections and deliberate distractor spans (negated / ruled-out entities), reflecting the mixed layout of real clinical encounter documents.

Stat	Value
Patients	10
Encounters	29
GT spans	171 (86 Condition · 85 Medication)
Distractor spans	14
Structured lab analytes	446 (35 unique, 100% LOINC coverage)
Text segments	815 (111 contain GT entities)
Encounter types	initial_consult · follow_up · specialist_letter · acute_visit · lab_review
Patient profiles	minimal · typical · complex (controls entity density)

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Configs

All configs share doc_id (e.g. "patient_01/encounter_01") and patient_id as join keys.

Config	Rows	Primary use
documents (default)	29	Full archive — document text, PDF, all GT annotations
chunks	815	NER evaluation — one row per document segment with inline entity annotations
patient_summary	10	Patient-level overview — aggregated GT spans, labs, encounter list

How configs relate

patient_summary ── patient_id ──► documents ◄── doc_id ──► chunks
                                  (source of truth)

Lab data lives in documents.structured_entities (filter to block == "lab_grid").

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Quick start

from datasets import load_dataset
import json

# ── Chunk-level NER (recommended starting point) ─────────────────────────
chunks = load_dataset("betterInnovation/SynthIPS", "chunks", split="test")

for chunk in chunks:
    entities = json.loads(chunk["gt_entities"])
    if not entities:
        continue
    for e in entities:
        span = chunk["markdown_text"][e["start"]:e["end"]]
        print(chunk["segment_type"], f"p{chunk['page_number']}",
              e["label"], repr(span), "|", e["kg_id"])

# ── Full-document NER (join chunks → documents for full text) ─────────────
docs = load_dataset("betterInnovation/SynthIPS", "documents", split="test")
doc = docs[0]

gt_spans = json.loads(doc["gt_spans"])
for e in gt_spans:
    span = doc["markdown_text"][e["start"]:e["end"]]
    print(e["surface"], "→", e["label"], "|", e["canonical"])

# ── Lab data ──────────────────────────────────────────────────────────────
labs = [e for e in json.loads(doc["structured_entities"])
        if e["block"] == "lab_grid"]
for lab in labs:
    print(lab["analyte"], lab["value"], lab["unit"], lab["flag"], lab["loinc"])

# ── PDF bytes (rendered natively in HF Data Viewer) ───────────────────────
# doc["pdf_bytes"] → {"bytes": b"...", "path": None}  (~70 KB, 3 pages each)

# ── Patient summary ───────────────────────────────────────────────────────
patients = load_dataset("betterInnovation/SynthIPS", "patient_summary", split="test")
for p in patients:
    print(p["patient_id"], p["profile"],
          p["n_encounters"], "encounters")
    print("  encounter types:", list(p["encounter_kinds"]))  # native Arrow list

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Column reference

documents config — 29 rows

One row per encounter. The default config. PDF renders natively in the HF Data Viewer.

Column	Type	Description
doc_id	string	"patient_01/encounter_01" — join key
patient_id	string	"patient_01" — join key
pdf_bytes	Pdf	Raw PDF (~70 KB, 3 pages). Renders as page viewer in HF Data Viewer.
markdown_text	string	Full document text as markdown — header, problem list, medication table, lab grid, narrative, signatures. start/end offsets in gt_spans point here.
patient_name	string
patient_profile	string	minimal / typical / complex
patient_sex	string	"M" / "F"
patient_dob	string	ISO date
anchor_icd10	string	Primary ICD-10 codes
clinical_hook	string	One-line clinical summary
encounter_id	string	"encounter_01"
encounter_kind	string	initial_consult / follow_up / specialist_letter / acute_visit / lab_review
encounter_title	string	Full encounter title
encounter_date	string	ISO date
provider_name	string
provider_role	string
provider_facility	string
gt_spans	string (JSON)	GT entity spans — start/end into markdown_text
distractor_spans	string (JSON)	Trap spans (negated / family history / hypothetical) — should NOT be extracted
structured_entities	string (JSON)	Structured block entities (lab grid, problem list, medications) with LOINC/ICD-10/ATC codes

chunks config — 815 rows

One row per document segment. markdown_text here is the segment's own slice of the full document text. Join on doc_id → documents for complete document context.

Column	Type	Description
doc_id	string	Join key → documents
patient_id	string
markdown_text	string	This segment's text. start/end in gt_entities are offsets into this field.
encounter_id	string
encounter_kind	string
patient_profile	string
chunk_id	string	Segment identifier within the encounter
page_number	int	Document page (1-indexed)
chunk_order	int	Reading order within the encounter
segment_type	string	"text" or "table"
gt_entities	string (JSON)	GT entities in this segment — start/end into markdown_text
distractor_entities	string (JSON)	Distractor spans in this segment

patient_summary config — 10 rows

One row per patient. Cross-encounter data pre-aggregated.

Column	Type	Description
patient_id	string
patient_name	string
sex	string	"M" / "F"
dob	string	ISO date
profile	string	minimal / typical / complex
anchor_icd10	string	Primary diagnoses
clinical_hook	string	One-line clinical summary
n_encounters	int
encounter_ids	list[string]	Native Arrow list — no json.loads needed
encounter_kinds	list[string]	Native Arrow list
all_gt_spans	string (JSON)	All GT spans across all encounters — filter by label for Condition / Medication
all_distractors	string (JSON)	All distractor spans
all_labs	string (JSON)	All structured lab analytes across all encounters

---

Entity span schema

Applies to gt_spans / distractor_spans (documents) and gt_entities / distractor_entities (chunks).

Field	Type	Description
surface	str	Exact mention text as it appears in the document (may be abbreviation: "HTN", "T2DM")
label	str	IPS type: Condition or Medication
start	int	Start char offset into markdown_text (document-level in documents, segment-level in chunks)
end	int	End char offset into markdown_text
kg_id	str	ICD-10 code (Condition) or ATC code (Medication)
canonical	str	Normalised entity name — use for normalisation scoring
section_kind	str	Source section: hpi · social_history · clinical_observation · assessment

Offset guarantee: markdown_text[start:end] matches surface after whitespace normalisation. Line-wrapping may insert \n between words — normalise with re.sub(r'\s+', ' ', span) before comparing.

Distractors share the same schema. They are contextually plausible entities that appear in the narrative but are not part of the patient's confirmed record (negated diagnoses, family history mentions, hypothetical treatments). A model that extracts them is producing false positives.

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Usage guide

1 · Chunk-level NER (primary track)

import json
from datasets import load_dataset
from nervaluate import Evaluator

chunks = load_dataset("betterInnovation/SynthIPS", "chunks", split="test")

all_gold, all_preds = [], []
for chunk in chunks:
    gold = [
        {"label": e["label"], "start": e["start"], "end": e["end"]}
        for e in json.loads(chunk["gt_entities"])
    ]
    preds = your_model(chunk["markdown_text"])  # same format
    all_gold.append(gold)
    all_preds.append(preds)

evaluator = Evaluator(all_gold, all_preds, tags=["Condition", "Medication"])
results, _ = evaluator.evaluate()

2 · Full-document NER

docs = load_dataset("betterInnovation/SynthIPS", "documents", split="test")

for doc in docs:
    gold = [
        {"label": e["label"], "start": e["start"], "end": e["end"]}
        for e in json.loads(doc["gt_spans"])
    ]
    preds = your_model(doc["markdown_text"])
    # ... accumulate and evaluate

3 · Distractor robustness (false-positive rate)

from rapidfuzz import fuzz

def fires_on(pred_surface, distractor_surface, threshold=85):
    return fuzz.token_set_ratio(pred_surface.lower(), distractor_surface.lower()) >= threshold

for doc in docs:
    distractors = json.loads(doc["distractor_spans"])
    preds = your_model(doc["markdown_text"])
    fp = sum(
        1 for d in distractors
        if any(fires_on(p["text"], d["surface"]) for p in preds)
    )
    fp_rate = fp / len(distractors) if distractors else 0

4 · Lab analyte extraction

for doc in docs:
    labs = [e for e in json.loads(doc["structured_entities"])
            if e["block"] == "lab_grid"]
    # Each lab: analyte, value, unit, flag, loinc
    # Score: precision/recall at analyte-name level against model output on markdown_text

5 · Entity normalisation scoring

Compare model output against canonical (normalised name) and kg_id (ICD-10 / ATC code).

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The 10 patients

ID	Name	Sex	DoB	Profile	Primary conditions
patient_01	Donald Wilson	M	1957-01-14	typical	T2DM, hypertension, dyslipidaemia
patient_02	Kimberly Hernandez	F	2000-01-31	minimal	Asthma, allergic rhinitis
patient_03	Charles Lopez	M	1943-07-09	complex	Heart failure, arrhythmia, T2DM
patient_04	Jennifer Martin	F	1978-11-23	typical	MDD, GAD
patient_05	Patricia Moore	F	1965-06-19	typical	Knee OA, chronic low back pain, dyslipidaemia
patient_06	Karen Thomas	F	1972-06-14	minimal	Primary hypothyroidism
patient_07	William Johnson	M	1947-07-17	complex	COPD, hypertension, smoking
patient_08	Thomas Lee	M	1955-11-14	complex	Diabetic nephropathy CKD3, prior MI, angina
patient_09	Emily Hernandez	F	1989-11-23	typical	GERD, anxiety
patient_10	Karen Wilson	F	1964-03-29	typical	Osteoporosis, RA, hypothyroidism

Profile controls entity density: minimal (1–3 problems/meds) · typical (3–6) · complex (6–12).

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Design notes

IPS entity types

SynthIPS covers the three primary sections of the IPS standard:

IPS section	Label in dataset	Identifier
Problems (conditions, diagnoses)	Condition	ICD-10 via kg_id
Medications	Medication	ATC code via kg_id
Results (lab analytes)	Lab	LOINC code via loinc field in structured_entities

Entity names in canonical follow the IPS-preferred normalised form. Abbreviations and synonyms (e.g. "HTN", "T2DM") are preserved in surface exactly as they appear in the document.

Document structure

Each encounter document contains several distinct sections: institution header, patient demographics, structured problem list, medication table, structured lab grid, and free-text narrative sections (HPI, social history, clinical examination, assessment & plan). This mixed structure — combining tabular and narrative content — reflects the layout of real clinical encounter documents and is a deliberate challenge for NER models.

markdown_text preserves this structure as formatted markdown. All start/end offsets in entity spans point into markdown_text.

Distractor design

Each encounter includes 0–1 deliberate trap spans: clinically plausible entities that appear in the narrative but are negated, belong to family history, are ruled out, or are hypothetical. Examples: "no evidence of X", "father had X", "we will consider X if...". These measure whether a model extracts only confirmed, patient-present entities.

Reproducibility

Every encounter document is deterministic given its layout_seed. Re-running the composer with the same source files produces byte-identical documents and GT annotations.

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Citation

@dataset{synthips2026,
  title   = {SynthIPS: Synthetic IPS NER Validation Dataset},
  author  = {Better Care},
  year    = {2026},
  url     = {https://huggingface.co/datasets/betterInnovation/SynthIPS},
  license = {CC BY 4.0}
}

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License

CC BY 4.0 — free to use, share, and adapt with attribution.