---
license: cc-by-4.0
language:
- en
pretty_name: Healthcare.pdf
task_categories:
- question-answering
tags:
- document-question-answering
- healthcare
- clinical
- pharmacy
- benchmark
- document-understanding
- pdf
- grounding
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test.parquet
---

# Healthcare.pdf — representative release (v1.0)

A PDF-grounding benchmark for healthcare document work: **25 expert-authored tasks** grounded in
**25 real healthcare documents**, covering **7 occupations** across clinical and pharmacy practice.

Each task puts a practitioner in a realistic situation, gives them a real document, and asks a sequence of
sub-questions that must be answered *from that document*. Answers are graded against a four-tier rubric.

| | |
|---|---|
| Tasks | 25 |
| Source documents | 25 (CDC Stacks, FDA drug labels) |
| Sub-questions | 152 |
| Rubric criteria | 1,513 |
| Occupations | 7 |
| Document pages | 350 |

## Loading

```python
from datasets import load_dataset

ds = load_dataset("CentificAIResearch/Healthcare.pdf", split="test")
row = ds[0]

row["case_setup"]                     # the practitioner's situation
row["sub_questions"][0]["question"]   # first sub-question
row["sub_questions"][0]["reference_answer"]
row["document"]                       # the source PDF (decoded)
row["rubric"]                         # grading criteria, G1-G4
row["anchors"]                        # where in the document the answers live
```

The source PDFs are embedded in the Parquet, and also available as plain files under `pdfs/`.

## Fields

| Field | Description |
|---|---|
| `task_id` | `persona/occupation/task_id` |
| `persona`, `occupation`, `aspect` | role and the workflow area the task belongs to |
| `doc_id`, `document_name`, `document_file`, `doc_pages` | source document identity |
| `document` | the source PDF |
| `case_setup` | the clinical/pharmacy situation the practitioner is in |
| `sub_questions` | list of `{q_no, question, reference_answer, task_types, anchor_ids, derivation, premises_grounded}` |
| `anchors` | list of `{id, element_type, title, locator_quote, page, answer_bearing}` — the document locations the task is grounded in; `sub_questions.anchor_ids` and `rubric.source_anchor_id` resolve against these |
| `rubric` | list of `{rubric_id, q_no, tier, grading_mode, kind, criterion, source_type, source_anchor_id, task_types}` |
| `n_subquestions`, `n_criteria` | counts |
| `difficulty_band`, `n_models_solved`, `n_models_all_criteria` | observed difficulty (see below) |

### Rubric tiers

- **G1 Domain** — healthcare-wide conformance (grounding, citation, no fabrication)
- **G2 Persona** — expectations for the persona
- **G3 Occupation** — expectations for the specific occupation
- **G4 Task** — the task's own content criteria, graded per sub-question

`grading_mode` is `match` (the answer must state this value), `entailed` (the answer must imply this step),
or `present` (a behaviour/format requirement over the whole deliverable).

### Task types

Sub-questions are tagged with a capability taxonomy (T1–T19) spanning foundational extraction
(value extraction, layout navigation), structural reading (table parsing, conditional logic),
advanced reasoning (abstention, quantitative reasoning, synthesis) and healthcare-specific work
(clinical safety, care-transition extraction, eligibility matching). All 18 types present in the source
pool are represented here.

## Difficulty

`difficulty_band` records how many of six frontier models solved each task, under a two-grader consensus.
A task is **solved** when every sub-question conveys the reference answer's key value or conclusion.

| Band | Models solving | Tasks |
|---|---|---|
| `unsolved` | 0 / 6 | 6 |
| `hard` | 1–2 / 6 | 5 |
| `mid` | 3–4 / 6 | 6 |
| `easy` | 5 / 6 | 4 |
| `ceiling` | 6 / 6 | 4 |

`n_models_all_criteria` is a stricter reading — how many models passed *every* rubric criterion, not just
conveyed the right answers. Across the release that is **8 of 150** model-task pairs versus 72 of 150 for
`solved`. The gap is the point: most answers that look right still miss rubric criteria.

The hard end is deliberately over-sampled relative to the source pool, so this subset is **harder than the
full benchmark** and its absolute scores should not be read as corpus-wide performance.

## Source data

- **CDC Stacks** (14 documents) — US public-domain government publications
- **FDA drug labels** (11 documents) — US public-domain regulatory documents

Documents were selected for containing determinate, answer-bearing content that a practitioner would act on.
Tasks and rubrics were authored against those documents and anchored to specific locations within them.

This release is the redistributable portion of a larger benchmark. A third track built on MIMIC-IV
radiology reports is **not** included: PhysioNet credentialed access forbids redistribution.

## Selection

The 25 were chosen from a 100-task redistributable pool to span difficulty, occupation and capability while
preserving the pool's model ranking (Spearman ρ = 0.956, no inversions among separable model pairs).
Rank fidelity was an explicit selection objective, so it is a design property rather than independent
evidence. One task per source document; at most two tasks per workflow aspect.

## Limitations

- **Not a random sample.** Deliberately over-weighted toward hard tasks; not an estimate of corpus-wide difficulty.
- **Two personas only.** Generalist clinicians (14) and pharmacists (11); the specialist track is excluded for licensing.
- **Answer keys are public.** Reference answers and rubrics ship with the tasks, so results are only meaningful for models that have not trained on this data. Treat it as a one-shot measurement and report the model's training cutoff.
- **Expert review is partial.** 6 of the 25 tasks were audited by practising clinicians as part of a broader
  purposive review; the remainder were not individually SME-reviewed.
- **Grader-dependent.** Difficulty metadata comes from LLM graders (two independent vendors, consensus
  required). Grader choice moves absolute numbers.

## Licence

**CC-BY-4.0.** Source documents are US government works in the public domain; the tasks, rubrics and
grounding annotations are original work released under CC-BY-4.0.

## Citation

```bibtex
@misc{healthcarepdf2026,
  title  = {Healthcare.pdf: a PDF-grounding benchmark for healthcare document work},
  author = {Centific AI Research},
  year   = {2026},
  url    = {https://huggingface.co/datasets/CentificAIResearch/Healthcare.pdf}
}
```