Enhancing accuracy in clinical laboratories: a study on error identification via delta check in high-volume settings.

Abstract

Purpose: Medical laboratories are integral to patient diagnosis, with approximately 70% of clinical decisions relying on laboratory results. Ensuring result accuracy requires robust quality assurance measures, including delta check alerts, which flag significant differences between consecutive test results within patients. This study aimed to assess the effectiveness of Delta Check alerts in identifying errors in mean corpuscular volume (MCV) within a high-volume tertiary care laboratory, thereby enhancing patient safety and diagnostic precision.

Design/methodology/approach: A cross-sectional study was conducted at The Aga Khan University Hospital, Pakistan from January to March 2024. All inpatient complete blood count (CBC) samples were included. A delta check event was defined as a change in MCV exceeding 5 femtoliter (fL) within 24 h. These events were classified as valid or invalid based on clinical relevance, with valid errors further categorized into pre-analytical and analytical errors. Data were analyzed using Microsoft Excel 365, and categorical variables were reported as frequencies and percentages.

Findings: Among 149,286 inpatient CBC samples analyzed, 1,779 (1.19%) MCV delta check alerts were generated. Of these, 1,694 (95.2%) were invalid, primarily due to blood transfusions (n = 1,013, 59.7%). The remaining 85 valid errors included wrong patient labeling (n = 64, 75.2%), sample dilution (n = 19, 22.3%) and insufficient sample volume (2, 2.3%). These findings highlight the utility of delta check alerts in identifying pre-analytical errors that could otherwise compromise result accuracy.

Research limitations/implications: This study underscores the value of delta check alerts in identifying pre-analytical issues, especially mislabeling and sample dilution, thereby strengthening laboratory quality assurance. Although many alerts were invalid, often due to transfusions, optimizing threshold settings may enhance system efficiency. Incorporating automated delta check processes into routine operations could provide an additional layer of protection against unnoticed errors. Future studies should examine the potential of machine learning to boost error detection while reducing false positives, ultimately improving both laboratory efficiency and diagnostic precision.

Practical implications: Implementing delta check alerts can enhance laboratory error detection, particularly in identifying pre-analytical issues such as mislabeling and sample dilution. Integrating automated alerts into routine workflows may improve result accuracy and reduce diagnostic errors.

Social implications: By improving laboratory quality assurance, delta check alerts contribute to enhanced patient safety, reduced diagnostic delays and improved healthcare outcomes. Strengthening error detection mechanisms supports public trust in laboratory testing and promotes better clinical decision-making.

Originality/value: This study provides valuable insights into the application of delta check alerts as an additional quality assurance measure in high-volume hematology laboratories. While most alerts were clinically explainable and deemed invalid, a small but significant proportion identified critical pre-analytical errors, underscoring the importance of delta checks in enhancing laboratory quality assurance. By integrating delta check alerts into routine practice, laboratories can improve error detection, minimize misdiagnosis and enhance overall patient safety.