Healthcare Prescription (JSON)
Synthetic e-prescription JSON for pharmacy routing and validation tests.
{
"rx_id": "RX-SAMPLE-001",
"patient": "PAT-001",
"medication": "Sampleatin 10mg",
"sig": "Take one tablet daily",
"quantity": 30
}
Specifications
- Domain
- healthcare
Testing contract
Expected to pass- Scenario
- Exercise Healthcare Prescription (JSON) in its healthcare workflow. Synthetic e-prescription JSON for pharmacy routing and validation tests.
- Expected result
- top-level keys are rx_id, patient, medication, sig, quantity; selected values: {"quantity": 30}. Declared feature checks: domain=healthcare.
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“Healthcare Prescription (JSON)” is a deterministic Novus Examples fixture for Data import. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: JSON · 149 bytes. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
Data fixtures document their exact quirks (delimiters, encodings, null handling, schema, and row counts) in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.
Code examples
import json
with open("prescription.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
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