Improving Realism of Synthetic Clinical Benchmarks Under Utility Constraints
A recent paper on arXiv (2608.06265v1) tackles the issue of synthetic clinical benchmarks for enterprise AI agents that, while passing utility assessments, lack structural realism, especially in privacy-sensitive healthcare environments with limited operational data. The authors introduce a strategy for revising benchmarks to enhance realism within utility constraints, focusing on modifying datasets to boost realism without compromising operational utility. This approach is applied to a care-gap benchmark created from Synthea-generated patients, processed through demonstration electronic health record workflows, mirroring the pipeline used for operational data. Realism is evaluated based on missingness structure, simplicity, structural plausibility, and population alignment. The initial benchmark is described as extremely sparse, featuring a sampled-pair missingness of 79.44% and only 12.75% of rows being active. The paper can be accessed on arXiv.
Key facts
- Paper arXiv:2608.06265v1
- Focus on synthetic clinical benchmarks for enterprise AI agents
- Benchmarks can pass utility checks but remain structurally unrealistic
- Privacy-sensitive healthcare settings limit access to operational data
- Formulates benchmark revision as utility-constrained realism improvement
- Uses Synthea-generated patients and demonstration EHR workflows
- Realism metrics: missingness structure, simplicity, structural plausibility, population alignment
- Baseline benchmark: sampled-pair missingness 79.44%, only 12.75% rows active
Entities
Institutions
- arXiv