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Establishing the laboratory error database: rationale, methodology, and a practical example.

Hikmet Can Çubukçu1, Oswald Sonntag2, Charles Lefèvre3

  • 1Department of Medical Biochemistry, Sincan Training and Research Hospital, Ankara, Türkiye.

Clinical Chemistry and Laboratory Medicine
|March 17, 2026
PubMed
Summary
This summary is machine-generated.

A new database, the Committee on Laboratory Error Database (C-LED), addresses patient safety by cataloging laboratory errors. It uses AI to provide accessible data on error types and clinical risks, enhancing diagnostic accuracy.

Keywords:
artificial intelligencedatabaseinformationlaboratory errorspatient safetytest interference

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Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Patient Safety

Background:

  • Laboratory errors significantly impact patient care, affecting 26-30% of cases.
  • A lack of accessible, comprehensive data on laboratory errors hinders error management.
  • Existing scientific literature lacks a centralized, open-access platform for error information.

Purpose of the Study:

  • To establish the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Committee on Laboratory Error Database (C-LED).
  • To create a continuously updated, evidence-based database of laboratory errors, their impacts, and clinical risks.
  • To enhance diagnostic accuracy and patient safety through accessible error data.

Main Methods:

  • A three-pillar information procurement strategy: literature, manufacturer data, and supplementary sources.
  • An AI-driven literature screening process using Claude Desktop and Google NotebookLM for systematic data extraction.
  • Case study analysis of haemolysis interference on cardiac troponin measurements across different platforms.

Main Results:

  • Demonstrated platform-specific interference patterns for haemolysis in cardiac troponin assays.
  • Interference magnitude was found to be concentration-dependent, with higher impact at lower analyte levels.
  • The AI-driven approach efficiently processed literature for comprehensive data extraction.

Conclusions:

  • The C-LED database provides a sustainable framework for managing laboratory error information.
  • AI integration enhances the efficiency and comprehensiveness of database construction.
  • The initiative aims to improve global diagnostic accuracy and patient safety by mitigating laboratory errors.