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Diagnostic stewardship monitoring systems: institutional and system-level approaches
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DOI：10.1080/10408363.2026.2666326.png)
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Abstract
En 中文
Diagnostic stewardship—performing the right test for the right patient at the right time—improves diagnostic accuracy and reduces healthcare resource waste. Various stewardship interventions have been introduced, yet their effects dissipate without systematic, sustained monitoring. One-time education and isolated system controls consistently show effect attenuation over time, and inappropriate ordering practices rapidly rebound once active oversight ceases. To date, the literature has focused on monitoring within single institutions; integration with population-level surveillance systems remains underexplored. This review proposes a framework linking institutional-level and system-level monitoring. At the institutional level, key performance indicators such as tests per patient-day and test-to-test ratios, combined with dashboard visualization, statistical process control charts, and cyclical audit-and-feedback structures, enable continuous surveillance of ordering patterns and drive behavioral change. Root cause analysis of monitoring data can identify specific drivers of over-ordering, and machine learning approaches show promise for predicting both overutilization and underutilization. These institutional tools, however, cannot track patients across facilities or assess population-level test appropriateness. At the system level, health insurance claims and administrative data enable macroscopic monitoring across entire populations. National experiences from Korea, Canada, and the United States demonstrate that ordering code pattern analysis can systematically identify inappropriate utilization—and that the structural design of reimbursement systems is more effective than voluntary recommendations in controlling low-value testing. The inherent limitation of claims data—the absence of test result values—can be partially overcome through linkage with other administrative databases. Bridging these two levels requires healthcare data standards and interoperability infrastructure, including Logical Observation Identifiers Names and Codes (LOINC), Nomenclature for Properties and Units (NPU), Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT), and Fast Healthcare Interoperability Resources (FHIR), yet practical barriers such as mapping quality variability, privacy constraints, and standardization costs persist. The scope of monitoring should extend beyond test ordering to encompass the entire total testing process, engaging diverse stakeholders across the testing continuum. With multidisciplinary workforce development, artificial intelligence-based clinical decision support, value-based reimbursement models, and rigorous multi-center studies, diagnostic stewardship monitoring can evolve into sustainable healthcare infrastructure that serves both individual patient safety and population health.
Keywords:
Diagnostic stewardship
laboratory monitoring
claims data
interoperability
clinical decision support
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