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From ordering to interpretation: a comprehensive framework for laboratory test indications

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Volume Title

dc.identifier.issn

1434-6621
1437-4331

dc.identifier.doi

10.1515/cclm-2025-1531

dc.identifier.other

dc.description.volume

64

dc.description.issue

5

Publisher

WALTER DE GRUYTER GMBH

DOI

10.1515/cclm-2025-1531
10.1515/cclm-2025-1531

Abstract

Objectives Clinical intent for laboratory testing (indication) is rarely recorded in structured form, limiting contextual interpretation, auditability, and utilization stewardship. We developed a comprehensive, and clinically applicable framework that standardizes laboratory test indications and links them to indication-dependent utilization and interpretation.Methods A structured literature review on utilization, appropriateness, and request rationale informed an iterative, consensus-based process with a multidisciplinary expert panel to develop and operationalize an indication taxonomy and attribute schema. Structural coherence was assessed by comparing semantic distance hierarchies derived from indication labels alone with an enriched multi-layer (layered prototype) representation incorporating these attributes. Use cases were applied to assess feasibility of indication-to-interpretation mapping.Results We defined 19 distinct indication types, grouped into five clusters across the clinical course: Initial Detection and Diagnostic Clarification, Disease Characterization and Prognosis, Therapy Guidance and Safety, Longitudinal Management and Reassessment, and Analytical and External Requirements. Each is specified with structured attributes and examples to support implementation. Semantic distance analyses supported a coherent hierarchy. Layered prototypes yielded more informative organization than labels alone, enabling context-dependent consolidation and guided deployment.Conclusions By providing explicit indication-to-interpretation mapping/logic, the framework closes a key gap in the total testing process between order entry and post-analytical interpretation. It supports context-specific decision limits, reporting logic, and stewardship analytics, and is amenable to formalization as a machine-readable ontology for interoperable implementation.

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