Partitioning All-Cause Pre-Analytical Laboratory Rejections: A 25-Month Retrospective Audit of Specimen Quality Failures with Six Sigma, IFCC, and Pareto Analysis
4 affiliations
- 1Department of Pathology, College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia
- 2College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia
- 3Department of Medical Education and Anatomy and Genetics, College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia
- 4Bacterial Diseases & Special Pathogens, Public Health Authority, Riyadh 13352, Saudi Arabia
Abstract
Objectives: Aggregate pre-analytical rejection rates conflate specimen-quality failures and administrative events, which require distinct mitigation protocols. We disaggregated rejection events to isolate specimen-quality failures and benchmarked them against global frameworks. Methods: We conducted a 25-month retrospective audit (July 2023 to August 2025) of test orders received by the Al Rass General Hospital laboratory. Using an AI-assisted classification protocol, we isolated pre-analytical Specimen Quality Failures from operational and administrative events. We mapped Specimen Quality Failure causes into IFCC WG-LEPS Quality Indicators (QIs) and evaluated via Six Sigma metrics, IFCC benchmarks, and Pareto analysis—stratified by laboratory section, sample type, priority, and work shift. Results: Of 905,018 test orders received, 35,476 were rejected (overall rejection rate 3.92%). However, the specimen-quality failures were 2.21% (σ = 3.51), with 15,495 entries excluded as operational events. Clotted samples (26.2%), hemolysis (23.0%), wrong sample type (20.2%), sample not received (7.8%), and insufficient volume (7.1%) together accounted for 84.3% of specimen quality failures (the Vital Few). Most individual categories scored Good (≥4.0) on Sigma, despite a pooled Sigma of 3.51 (Acceptable). Benchmarking against IFCC WG-LEPS targets showed that 6 of 13 quality indicators, including clotted sample, wrong sample type, and insufficient volume, fell Below Minimum, demonstrating divergent absolute internal process capability metrics and population-relative external benchmark performance signals. STAT samples had a lower specimen quality failure rate than Routine samples (1.25% vs. 2.51%). Although the morning shift accounted for 80.98% of total rejections, only 53.4% represented true specimen quality failures. Conclusions: Headline laboratory rejection rates are inflated by operational and administrative events, necessitating partitioned reporting models. While individual Westgard Sigma scores demonstrate satisfactory absolute process capability across most specimen quality failure categories, cross-referencing against population-relative IFCC targets contextualizes these results and identifies areas performing in the lower international quartiles. Quality improvement strategies require structural changes and workforce training.
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BibTeX
@article{arain2026partitioning,
title = {{Partitioning All-Cause Pre-Analytical Laboratory Rejections: A 25-Month Retrospective Audit of Specimen Quality Failures with Six Sigma, IFCC, and Pareto Analysis}},
author = {Shoukat Ali Arain and Abdullah Naji Alhawil and Shahid Akhtar Akhund and Mahnoor Ali and Salman Aldosari and Abdulwahab Z. Binjomah},
journal = {Journal of clinical medicine},
year = {2026},
month = oct,
doi = {10.3390/jcm15197663},
url = {https://europepmc.org/article/PMC/PMC13666386},
note = {CC BY},
}
RIS
TY - JOUR
TI - Partitioning All-Cause Pre-Analytical Laboratory Rejections: A 25-Month Retrospective Audit of Specimen Quality Failures with Six Sigma, IFCC, and Pareto Analysis
AU - Shoukat Ali Arain
AU - Abdullah Naji Alhawil
AU - Shahid Akhtar Akhund
AU - Mahnoor Ali
AU - Salman Aldosari
AU - Abdulwahab Z. Binjomah
PY - 2026
DA - 2026/10/01
JO - Journal of clinical medicine
DO - 10.3390/jcm15197663
UR - https://europepmc.org/article/PMC/PMC13666386
AB - Objectives: Aggregate pre-analytical rejection rates conflate specimen-quality failures and administrative events, which require distinct mitigation protocols. We disaggregated rejection events to isolate specimen-quality failures and benchmarked them against global frameworks. Methods: We conducted a 25-month retrospective audit (July 2023 to August 2025) of test orders received by the Al Rass General Hospital laboratory. Using an AI-assisted classification protocol, we isolated pre-analytical Specimen Quality Failures from operational and administrative events. We mapped Specimen Quality Failure causes into IFCC WG-LEPS Quality Indicators (QIs) and evaluated via Six Sigma metrics, IFCC benchmarks, and Pareto analysis—stratified by laboratory section, sample type, priority, and work shift. Results: Of 905,018 test orders received, 35,476 were rejected (overall rejection rate 3.92%). However, the specimen-quality failures were 2.21% (σ = 3.51), with 15,495 entries excluded as operational events. Clotted samples (26.2%), hemolysis (23.0%), wrong sample type (20.2%), sample not received (7.8%), and insufficient volume (7.1%) together accounted for 84.3% of specimen quality failures (the Vital Few). Most individual categories scored Good (≥4.0) on Sigma, despite a pooled Sigma of 3.51 (Acceptable). Benchmarking against IFCC WG-LEPS targets showed that 6 of 13 quality indicators, including clotted sample, wrong sample type, and insufficient volume, fell Below Minimum, demonstrating divergent absolute internal process capability metrics and population-relative external benchmark performance signals. STAT samples had a lower specimen quality failure rate than Routine samples (1.25% vs. 2.51%). Although the morning shift accounted for 80.98% of total rejections, only 53.4% represented true specimen quality failures. Conclusions: Headline laboratory rejection rates are inflated by operational and administrative events, necessitating partitioned reporting models. While individual Westgard Sigma scores demonstrate satisfactory absolute process capability across most specimen quality failure categories, cross-referencing against population-relative IFCC targets contextualizes these results and identifies areas performing in the lower international quartiles. Quality improvement strategies require structural changes and workforce training.
SN - 2077-0383
ER -