General Practice Preventive Care Eligibility and Timing Constraint Verification Benchmark
#eligibility constraint verification #timing compliance assessment #clinical decision validation #primary care preventive screening #preventive care eligibility assessment #healthcare scheduling compliance
- 5000
- 0.3GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-11-04
AI Analysis & Value Prop
This JSON benchmark records patient age, risk factors, prior screening or vaccination history, proposed preventive measures, and the applicable criteria, timing intervals, and exclusions. Covering common screening and preventive care decisions in general practice, its records include an eligibility outcome, assessments of individual constraints, and a decision rationale. It supports evaluation of whether models can determine eligibility, timing, and exclusions from the information and rules provided.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| patient_context | object | Patient characteristics and preventive care history used to assess the proposed measure. |
| decision_rationale | string | Concise explanation of the final eligibility assessment, including any decisive timing or exclusion factors. |
| eligibility_status | string | Final benchmark assessment of whether the proposed preventive measure satisfies the supplied constraints. |
| constraint_assessments | array | Assessment of each relevant applicability constraint against the patient's information. |
| applicability_constraints | object | The supplied criteria, timing rules, and exclusions governing whether the proposed measure is appropriate. |
| proposed_preventive_measure | object | The screening, vaccination, or other preventive service being considered for the patient. |
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 information does the benchmark contain?
- Each record combines patient context, a proposed preventive measure, and its applicability constraints. It also provides an eligibility status, assessments of relevant constraints, and a decision rationale. Patient information may include age, risk factors, and prior screening or vaccination history.
- Which capabilities does it evaluate?
- It evaluates whether a model can compare patient information with supplied eligibility criteria, timing intervals, and exclusions, determine whether a proposed measure satisfies those constraints, and identify the factors supporting its decision.
- Are the sample count and source guidelines specified?
- The information currently provided does not specify the sample count or guideline sources. Before use, consult the accompanying version notes, annotation documentation, and source information to establish the evaluation scope.
- Can it be used directly for clinical care or model training?
- The dataset is positioned as a domain evaluation benchmark for model validation, error analysis, and research. It is not a substitute for clinical guidelines or professional judgment. Training suitability and permitted use depend on the license and terms supplied by the publisher.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={General Practice Preventive Care Eligibility and Timing Constraint Verification Benchmark},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/01236200240f0c5c78cca77c41b8169c},
urldate={2026-10-13},
keywords={general practice preventive care dataset,preventive care benchmark,screening eligibility assessment,vaccination eligibility evaluation,clinical constraint reasoning,preventive care decisions,medical AI evaluation,JSON medical data},
version={1.0}
}Using this in research? Please cite us.
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