Augmenting Healthcare Systems for Pandemic Preparedness: A Lean Six Sigma Perspective
Notice bibliographique
Résumé
Background: Past global healthcare crises have vividly exposed major vulnerabilities in healthcare systems, including inefficiencies in hospital operations, delayed response times, and overburdened infrastructure. Traditional hospital systems that were built for routine care are sometimes not resilient or adaptable in the face of such crises, which resulted in global operational failures in healthcare systems. This study rigorously investigates how Lean Six Sigma (LSS) principles can be incorporated into conventional hospital operations to enhance the resilience of hospital infrastructures, streamline operations in time sensitive situations with precision in care, and improve waste reduction while being adaptable and sustainable, ensuring pandemic-preparedness. Methods: A comprehensive literature based-analysis was conducted using COVID-19 as a benchmark to evaluate hospital response strategies, failures, and influencing factors contributing to failure. This includes the critical assessment of ethical disruptions, operational weaknesses and healthcare business models. LSS principle applications, i.e., DMAIC, Value Stream Mapping, SIPOC, FMEA, and Control Charts were explored for facilitating efficient care, crisis response, and policy integration. Case studies from various regions were used to support the comparative analysis and emerging insights. Results: Findings show that adoption of LSS tools in the most vulnerable aspects of healthcare—like patient triage, supply chain optimization, and controlling and reducing mortality—can bring measurable improvements. Despite evidence of effectiveness, there are institutional barriers like capital constraints, resistance to change, data inconsistencies and vulnerabilities, and a lack of uniform legislative framework that impedes widespread LSS adoption. Most importantly, integrating data-driven LSS resulted in enhanced surge responsiveness and ethical compliance within the national healthcare frameworks and policies. Conclusion: LSS offers adaptable and scalable methodology to re-engineer conventional hospital operations and pandemic preparedness. The emphasis on ‘kaizen’ (continuous improvement), data-informed decision making, and focus on precision aligns with the needs of healthcare systems as revealed by recent crises. To unlock the potential for future preparedness, healthcare policies and systems must focus on institutionalizing LSS across public and private sectors through strategic investment, education, and cross-sector collaborations. This study provides a comprehensive framework for the policymakers, governments, epidemiologists, doctors, and hospital business managers for building resilient, efficient, and pandemic-ready hospitals.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».