B-138 Assessing Pre-Analytical Error Prevalence and Associated Costs: Findings from a Multinational Survey and Literature Review
Bibliographic record
Abstract
Abstract Background Errors in the pre-analytical phase of the total testing process account for approximately 60-70% of all laboratory diagnostic errors, making them the most common quality failures in the laboratory testing process. The prevalence and costs associated with the errors, including mistakes in patient identification, inappropriate specimen collection, and improper sample handling, remain uncertain. Methods We performed a primary and secondary research on prevalence and costs of pre-analytical errors (PAE). Initially, we conducted a survey during September-October 2024 among a multi-national group of academic laboratory directors (MD, PhD or MD/PhD) in the North America, Asia and Africa, focusing on their insights regarding the prevalence of PAEs in their local setting. Subsequently, based on the insights from this survey, we formulated the search terms to sift through current evidence on the prevalence of PAEs and their associated direct and indirect costs. To do so, we screened MEDLINE (via PubMed) and Google Scholar for peer-reviewed studies, published in English between 2000 and 2024. The direct costs of PAEs included personnel time and resources used for resampling, retesting, and investigating/reporting PAEs. Indirect costs were reported to arise from treating safety failures and/or extended hospital stays due to incorrect or delayed diagnosis or treatment. Based on these findings, we then conducted a narrative review. Results Thirteen of the addressed directors responded to our survey. 69% (9/13) reported to measure the frequency of PAEs with a prevalence of 3% or less of the total samples. In contrast, among those 31% (4/13) who did not report measuring the frequency of PAEs, 50% (2/4) estimated their frequency at 3% or higher and 50% (2/4) expressed uncertainty. In the subsequent literature research, a total of 744 abstracts/titles were screened, resulting in the inclusion of 21 articles for detailed analysis. Thirteen of these studies focused on the prevalence of PAEs, and 8 studies examined the associated costs. The reported prevalence of PAEs varied, with sample rejection rates ranging from 0.37% to 6.61%. In clinical laboratories, the direct costs of PAEs may constitute up to 10% of the annual testing budget. Furthermore, when considering both direct and indirect costs, PAEs can account for 0.2% to 1.2% of total hospital operating costs, compared to 2-3% of hospital operating costs for overall laboratory costs. Conclusion Our research suggests that prevalence and costs of PAEs vary considerably, depending on patient populations and laboratory settings. Nevertheless, PAEs impose a significant financial burden on healthcare budgets. Determining and benchmarking prevalence and costs of PAEs can help laboratories to detect inadequate sample quality more easily, improve the quality of laboratory testing process and patient care and reduce overall cost of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".