Lean six sigma process improvement in specimen receiving to improve stat chemistry turnaround times
Notice bibliographique
Résumé
As a consequence of stat turnaround times (TATs) chronically exceeding 60 minutes, our laboratory was facing pressure to divert limited resources toward the implementation of an emergency department satellite laboratory. Peer-reviewed literature in clinical laboratory quality assurance and improvement indicates that between 60-70% of errors occur at the pre-analytical level. Thus, we sought to improve overall TATs by focusing on reducing pre-analytical lag times. Lean six sigma process improvement owes its origins to industry, and may be universally applied in healthcare settings to improve outcomes. We report the application of Lean six sigma process improvement tools in the clinical laboratory specimen accession and processing area of a busy tertiary care center to improve chemistry stat TATs. The prospective before-and-after redesign encompassed a detailed evaluation of existing system, assessment of established monitors and historical data, formulation and implementation of a plan, and post-move data collection and analysis. Allocation of laboratory space was based on Lean six sigma quality improvement methods. Test TAT and volumes were obtained from the LIS. Spaghetti diagrams were utilized to assess workflow in the existing space and in layout planning for the new space. An assessment of the pre-analytical steps in the receiving and processing area, in tandem with pre and post move Pareto chart data enabled the calculation of the reduction of defects per million opportunities that could be ascribed to this effort. 12 months mean ED CMP TATs before the move was 44.4 minutes with 90% of results reported in 60 minutes or less; after the move this improved to a mean of 37.1 minutes with 90% of results reported in 49 minutes or less. 12-month ED troponin mean TAT was 49.5 minutes with 83% of results reported in 60 minutes or less; after the move this improved to mean TAT of 43.4 minutes with 90% of results reported in 55 minutes or less. Given seven touch points per result, this project enabled a 75% reduction in defects per million opportunities. Lean-six sigma tools facilitated the identification and elimination of inefficiencies in specimen receiving to enable sustained improvements in TATs. Thus, defining and measuring problems, planning, taking necessary steps and implementing them are effective techniques to improve throughput in pre-analytical specimen handling. The one-time expenses associated with the moves were minimal, and the costavoidance of satellite laboratory oversight and operation is substantial. Lean six sigma techniques can be applied in a cost-effective manner to minimize preanalytical wastes and improve patient care.
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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 | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».