B-138 Assessing Pre-Analytical Error Prevalence and Associated Costs: Findings from a Multinational Survey and Literature Review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».