Augmenting Healthcare Systems for Pandemic Preparedness: A Lean Six Sigma Perspective
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".