A-180 Counting CAP deficiency Caused by Clerical errors: a regional study
Notice bibliographique
Résumé
Abstract Background Proficiency testing (PT) is a critical component of quality assurance and a laboratory accreditation requirement. PT ensures that laboratory results are comparable to those of peers using the same instruments and methods. Failures may indicate inaccurate patient results, requiring formal investigation and corrective action by the testing laboratory. Depending on the accreditation body, consecutive PT deficiencies can lead to mandatory cessation of testing. Clerical errors are among the most common causes of unacceptable PT grades. While many laboratory instruments directly communicate with the laboratory information system for electronic result transfer, PT result reporting often involves manual transcription. This study aims to determine the frequency of clerical errors associated with unacceptable PT grades and to implement quality improvement strategies where necessary. Methods All PT results submitted to the College of American Pathologists (CAP) between January 2020 and September 2024 (57 months) were reviewed for three chemistry laboratories (designated as A, B, and C) in Saskatoon, Saskatchewan, Canada. The number of unacceptable CAP submissions was tabulated and compared across the three laboratories. Clerical errors contributing to unacceptable submissions were classified into three subcategories: direct transcription errors, incorrect method codes, and incorrect instrument codes. Chi-squared analysis was performed to assess statistical significance. The CAP PT reporting process in the core chemistry laboratory was reviewed through discussions with senior technologists responsible for result submission. Results A total of 32,868 results were submitted to CAP during the study period: Laboratory A (16,456), Laboratory B (2,688), and Laboratory C (13,724). The overall percentage of unacceptable CAP submissions was 0.83% (273/32,868). Of these, 44% (120/273) were attributed to clerical errors, including direct transcription errors (43/120), incorrect method codes (49/120), and incorrect instrument codes (27/120). Laboratory A had a significantly lower percentage of clerical errors [0.18% (30/16,456)] compared to Laboratory B [0.56% (15/2,688)] and Laboratory C [0.55% (75/13,724)] (p < 0.001, Chi-squared = 30, df = 2). No significant difference was observed between Laboratories B and C. A process review revealed that Laboratory A was the only site utilizing a secondary verification step, where a second technologist reviewed PT results before submission. Conclusion Clerical errors are a major contributor to CAP deficiencies. Laboratory A, which implements a secondary verification process for manually entered PT results, had significantly fewer clerical errors. This practice aligns with established procedures for manual patient result entry. Consequently, Laboratories B and C have now adopted a similar verification process. Follow-up studies will evaluate whether this intervention reduces clerical errors.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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.
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