| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| task_categories: |
| - text-generation |
| - table-question-answering |
| tags: |
| - text-to-sql |
| - clinical |
| - fhir |
| - healthcare |
| - synthetic |
| - duckdb |
| - synthea |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: train.jsonl |
| - split: heldout_familiar |
| path: heldout_familiar.jsonl |
| - split: heldout_unseen |
| path: heldout_unseen.jsonl |
| --- |
| |
| # FHIR-to-SQL with Database-Resolved Clinical Terminology |
|
|
| Natural-language hospital questions paired with a **structured JSON query plan and compiled DuckDB |
| SQL**, over a FHIR-derived clinical schema. Built entirely from **synthetic** Synthea patients — no |
| real patient data. |
|
|
| Companion to the paper *Plan-Then-Compile: Turning a General-Purpose Coder Model into a FHIR Data |
| Analyst*. |
|
|
| - **Code & paper:** https://github.com/adelelsayed/fhirsql-reasoning-sql |
| - **Adapters:** https://huggingface.co/adelelsayed1991/fhirsql-reasoning-sql-adapters |
|
|
| ## What makes this dataset different |
|
|
| Gold SQL **never embeds a literal clinical code**. Every query resolves its concept at runtime |
| through a lookup against a `valuesets` terminology table: |
|
|
| ```sql |
| WITH resolved AS ( |
| SELECT code, code_system FROM valuesets |
| WHERE table_name = 'procedure' AND display ILIKE '%Depression screening (procedure)%' |
| ) |
| SELECT DISTINCT patient_id |
| FROM procedure, resolved |
| WHERE procedure.code = resolved.code AND procedure.system = resolved.code_system |
| AND status = 'completed' |
| ``` |
|
|
| This turns terminology resolution from a memorization problem into a queryable one, and makes |
| generalization to clinical concepts absent from training measurable. |
|
|
| ## Splits |
|
|
| | Split | Rows | Answerable | Abstention | |
| |---|---:|---:|---:| |
| | `train` | 10,696 | 9,680 | 1,016 | |
| | `heldout_familiar` | 9,300 | 8,284 | 1,016 | |
| | `heldout_unseen` | 2,156 | 1,140 | 1,016 | |
|
|
| Held-out splits come from a **disjoint 6,383-patient population** (training: 18,999 patients; zero |
| `patient_id` overlap). |
|
|
| - **`heldout_familiar`** — clinical concepts also used in training, different patients. Isolates |
| population generalization. **96.5% of its answerable rows are byte-identical `(question, gold_sql)` |
| pairs from `train`**, so it measures whether learned queries stay correct on new data; it cannot |
| distinguish memorization from generalization. |
| - **`heldout_unseen`** — 84 clinical concepts appearing **nowhere** in training (verified by set |
| intersection; zero shared question/query pairs). This is the split for measuring transfer. |
| |
| ⚠️ **The 1,016 abstention rows are the same rows in all three splits.** Unanswerable questions |
| reference no patient data, so an identical set was reused. Abstention metrics computed on the |
| held-out splits therefore measure *retention* of trained refusal behavior, not held-out refusal |
| generalization. |
| |
| ⚠️ **Execution match is weakly discriminative on `heldout_unseen`.** Its concepts are rare (1–3 |
| patients each) and gold answers are small integers: 53.9% of within-archetype concept pairs return |
| identical results, so a wrong concept can go undetected. Text-match metrics are not subject to this. |
|
|
| ## Fields |
|
|
| | Field | Description | |
| |---|---| |
| | `question` | Natural-language question, phrased for one of 11 hospital roles | |
| | `target` | Training target: `json.dumps(plan, indent=2)` + fenced ` ```sql ` block | |
| | `gold_plan` | Structured query plan (entities, joins, constraints, aggregation, abstain) | |
| | `gold_sql` | Compiled DuckDB SQL, or the literal `UNANSWERABLE` token | |
| | `archetype_id` | Query archetype (82 total: 64 answerable + 18 unanswerable) | |
| | `tier` | Difficulty tier 1–4, or 5 for unanswerable | |
| | `persona` | Hospital role the phrasing targets | |
| | `concept_display` | The clinical concept instantiated (empty for structural/unanswerable) | |
| | `schema_ref` | Schema the query is written against | |
|
|
| ## Provenance |
|
|
| Every answerable row's gold SQL was **executed against the real database** and kept only if it ran |
| without error and returned at least one row, then checked for repeated-execution stability. |
| Questions come from 160 role-appropriate phrasings authored with LLM assistance under the author's |
| direction, then parameterized by concept. |
|
|
| `schema.sql` is the exact schema injected into every prompt. `valuesets_ddl.sql` is an ablation |
| fragment that was **not** part of the prompt during the published runs — see the paper, §5.6. |
|
|
| ## Limitations |
|
|
| Synthetic data only; one question-generation distribution (templated phrasing, not free-form |
| clinician language); terminology resolution is substring matching against a corpus-derived |
| dictionary with one display string per code, so synonymy, hierarchy, and ambiguity are out of scope |
| (ambiguous concepts are excluded from gold). Some regulatory questions say "dispensed" but are |
| answered from `MedicationRequest` (prescription orders). Full discussion in the paper's §7. |
|
|
| ## Citation |
|
|
| See `CITATION.cff` in the GitHub repository. |
|
|
