A model to increase automation and support reliable interpretation of clinical chemistry tests

OBJECTIVES: Pathological conditions may result in clinical chemical values exceeding the linearity range of laboratory methods. In clinical practice, not all tests require exact numerical values for patient management, as useful information can still be provided when results are reported to a defined cutoff. This study describes an approach to reduce unnecessary dilutions by identifying tests that can be reported above a defined threshold. METHODS: Eighty-six clinical chemistry assays on Cobas 8000 (middleware: Infinity) were reviewed at the Laboratory Medicine of the University Hospital of Padua. Fifteen analytes requiring manual dilution beyond predefined automated limits were selected. A multi-step workflow was implemented, including literature review, clinician and staff engagement, definition of upper reporting limits, middleware customisation, and retrospective and prospective performance evaluation. Key outcomes included turnaround time (TAT), auto-release rate, manual dilution frequency, estimated costs, and clinician requests for exact values. RESULTS: Literature review and clinician feedback supported the definition of upper reporting limits beyond which exact values were not routinely required, except for lactate dehydrogenase (LDH), where oncologic follow-up necessitated precise reporting. Implementation of the new rules eliminated most manual dilutions, substantially reducing costs and improving efficiency. P90 TAT decreased for samples above cutoff values in most analytes between 2023 and 2025. LDH was the only analyte for which manual dilution was not completely eliminated in 2025, with a residual rate of 3.38 per 1000 tests (vs. 7.32 per 1000 in 2023). Clinician feedback confirmed the appropriateness of the new reporting rules. CONCLUSIONS: This study shows that revising validation rules to report results above predefined thresholds provides clinically meaningful information while reducing unnecessary manual procedures. This model supports appropriate patient management and improves efficiency by enhancing process automation, consistency, and decision-making.