Clin Chem Lab Med. 2026 Mar 16. doi: 10.1515/cclm-2026-0174. Online ahead of print.
ABSTRACT
Laboratory errors represent a critical yet underestimated threat to patient safety, with 26-30 % of reported errors adversely affecting patient care. Despite extensive scientific literature, no sustainable, open-access platform exists that allows laboratory professionals to rapidly access comprehensive information on error types, bias magnitudes, and clinical risks for specific analytes and platforms. To address this gap, the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) established the Committee on Laboratory Error Database (C-LED). C-LED aims to develop and maintain a continuously updated, evidence-based database covering laboratory errors, their impacts on test results, severity of potential harm, and supporting references across multiple analytical platforms and assay generations. C-LED employs a novel three-pillar information procurement strategy encompassing literature knowledge, manufacturer data, and supplementary sources, including incident reports and external quality assessment data. The literature procurement utilizes an innovative two-stage AI-driven approach: Stage 1 employs Claude Desktop with Model Context Protocol integration for systematic PubMed and Crossref screening, while Stage 2 combines Google NotebookLM and Claude Desktop for comprehensive full-text data extraction using standardized prompts and metadata structures. A case study examining haemolysis interference on cardiac troponin measurements demonstrates the approach’s value, revealing platform-specific patterns ranging from minimal interference in contemporary high-sensitivity assays to severe bias in older platforms. The study identified that interference magnitude is concentration-dependent, with greater impacts at lower analyte baselines. By combining institutional governance, AI-driven efficiency, and multiple information sources, C-LED aim to establish a sustainable framework for database construction, ultimately enhancing diagnostic accuracy and patient safety globally.
PMID:41843983 | DOI:10.1515/cclm-2026-0174