Enhancing Hospital Pharmacy Operations Through Lean and Six Sigma Strategies: A Systematic Review
Notice bibliographique
Résumé
Hospital pharmacies are integral to the healthcare system, and evaluating the factors influencing their efficiency and service standards is imperative. This analysis offers global insights to assist in developing strategies for future enhancements. The objective is to identify the optimal Lean Six Sigma methodologies to improve workflow and quality of hospital pharmacy services. A strategic search, aligned with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, encompassed an extensive range of academic databases, including Scopus, PubMed/Medline, Web of Science, and other sources for relevant studies published from 2009 to 2023. The focus was on management tactics and those examining outcomes, prioritizing publications reflecting pharmacy operations management's state. The quality of the selected articles was assessed, and the results were combined and analyzed. The search yielded 1,447 studies, of which 73 met the inclusion criteria. The systematic review found a low to moderate overall risk of bias. The number of publications rose during the coronavirus disease (COVID-19) outbreak. Among studies, research output in the United States of America represented 26% of the total. Other countries such as Indonesia, Spain, Canada, China, Saudi Arabia, the United Arab Emirates, and the United Kingdom also made significant contributions. Each country accounted for 12%, 8%, 7%, 5%, 5%, 5%, and 5%, respectively. The pharmacy journals led with 26 publications, and healthcare/medical with 14. The quality category came next with 12 articles, while seven journals represented engineering. Studies used empirical and observational methods, focusing on practice quality enhancement. The process control plan had 26 instances, and the define, measure, analyze, improve, and control (DMAIC) was identified 13 times. The sort, set in order, shine, standardize, and sustain (5S) ranked third, totaling seven occurrences. Failure mode and effects analysis (FMEA) and root cause analysis were moderately utilized, with six and four instances, respectively. Poka-Yoke (mistake-proofing measures) and value stream mapping were each counted three times. Quality improvement and workflow optimization dominated managerial strategies in 22 (30.14%) studies each, followed by technology integration in 15 (20.55%). Cost, patient care, and staffing each featured in three (4.11%) studies, while two (2.74%) focused on inventory management. One (1.37%) study each highlighted continuing education, collaboration, and policy changes. Analysis of the 73 studies on Lean and Six Sigma in hospital pharmacy operations showed significant impacts, with 26% of studies reporting decreased medication turnaround time, 15% showing process efficiency improvements, and 11% each for enhanced inventory management and bottleneck/failure mode reduction. Additionally, 9% of studies observed decreased medication errors, 8% noted increased satisfaction and cost savings, 6% identified enhancements in clinical activities, 3% improved prescription accuracy, 2% reduced workflow interruptions, and 1% reported increased knowledge. Also, this study has identified key strategies for service delivery improvement and the importance of quality practices and lean leadership. To the best of the author's knowledge, this research is believed to be the first in-depth analysis of Lean and Six Sigma in the hospital pharmacy domain, spanning 15 years from 2009 to 2023.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,010 |
| Bibliométrie | 0,015 | 0,014 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».