Observational studyPloS one2026
Pre-analytical errors in a high-volume Bangladeshi diagnostic centre: Prevalence, workload impact, and mitigation strategies.
Observational study in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Best practices in sample management & pre-analytical quality control: overcoming challenges in resource-limited laboratory settings.Tropical medicine and health · 2026Review
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPre-analytical errors are the most frequent cause of laboratory mistakes, accounting for nearly half of all diagnostic inaccuracies worldwide. These errors can invalidate test results, delay clinical decisions, and waste valuable healthcare resources, particularly in resource-limited, high-volume diagnostic laboratories. This study aimed to assess the prevalence, contributing factors, and severity of pre-analytical errors in a large diagnostic centre in Bangladesh.
methodsAn observational, cross-sectional study was conducted over two months in the Biochemistry and Immunology Laboratories of a high-volume diagnostic centre in Dhaka, Bangladesh. Data from 195 documented pre-analytical errors and a structured survey of 27 laboratory staff were analysed. Errors were classified into minor, moderate, or major using definitions adapted from ISO 15189:2022 and WHO guidelines. Descriptive statistics and Chi-square tests were performed to explore associations between workload level (≥ 931 samples/day) and error frequency, with p < 0.05 considered statistically significant.
resultsThe most frequent errors were sample misplacement (38.5%) and incorrect labelling (17.9%). The sample collection (42.6%) and pick-and-drop (38.5%) units contributed the majority of errors. Morning shifts (65.1%) and high-workload days (70.8%) showed higher error frequencies, with a statistically significant association between workload and error occurrence (χ² = 121.093, p < 0.001). Major errors accounted for 37.4% of incidents.
conclusionPre-analytical errors remain a critical threat to diagnostic accuracy in resource-limited laboratories. Improving workflow organization, implementing barcoding and automation, and strengthening staff training and workload management can substantially reduce error rates and enhance patient safety in high-throughput clinical settings.
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