Specialty Referral Intake Constraint Validation Benchmark

#referral constraint verification #compliance classification #violation detection #specialty referral management #referral intake compliance #clinical workflow validation

5000
0.4GB
JSON
CATL
MOBIUSI INC

Updated:2026-10-14

AI Analysis & Value Prop

This benchmark represents specialty referral information, intake policies, and validation outcomes in structured JSON. It covers referrals that meet intake criteria as well as cases with missing information, policy conflicts, or triage mismatches. Labels for eligibility, triage alignment, missing requirements, and rule violations support evaluation of models' understanding and constraint reasoning for specialty clinic referrals. It is suitable for healthcare model benchmarking, rule validation, and referral workflow research.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
is_eligible	boolean	Indicates whether the referral meets the specialty eligibility criteria based on the available information.
triage_matches	boolean	Indicates whether the referral's urgency and care pathway match the specialty's triage requirements.
intake_decision	string	Recommended disposition of the referral based on the validation findings.
referral_record	object	Submitted referral details, including clinical context and the requested specialty care.
rule_violations	array	Applicable policy rules that the referral fails to satisfy or conflicts with.
acceptance_policy	object	Intake requirements and applicable eligibility, exclusion, and triage rules for the requested specialty.
validation_status	string	Overall outcome of checking the referral against the applicable intake policy.
validation_summary	string	A concise explanation of the validation outcome and recommended disposition.
missing_requirements	array	Required information that is absent or insufficient in the referral.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What data does the benchmark include?: The structured JSON records include referral information, specialty intake policies, and validation outcomes. Labels cover eligibility, triage alignment, missing requirements, rule violations, and recommended intake decisions.

Does the benchmark include only referrals that meet intake criteria?: No. It includes eligible cases as well as cases with missing information, conflicts with intake rules, or mismatched triage pathways, enabling evaluation across different validation outcomes.

Which tasks is the benchmark suitable for?: It supports referral constraint verification, eligibility assessment, triage matching, missing requirement detection, and rule violation detection. It can also be used for healthcare model benchmarking and workflow research.

How can I access the data and confirm its license?: Check the dataset product page for available formats, delivery options, and license terms. If usage rights, redistribution restrictions, or commercial use terms are not specified, confirm them with the provider before use.

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Cite this Work

@dataset{Mobiusi2026,
  title={Specialty Referral Intake Constraint Validation Benchmark},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/449f9bdaa67c3f8f7eac091f3e6bfb0d},
  urldate={2026-10-12},
  keywords={specialty referral dataset,referral intake rules,healthcare constraint reasoning,referral validation benchmark,specialty clinic intake,referral eligibility assessment,triage matching evaluation,medical JSON data},
  version={1.0}
}

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