Role of Lean Six Sigma approach for enhancing the patient safety and quality improvement in the hospitals
Bibliographic record
Abstract
The Lean Six Sigma is one the most effective methods of total quality management to continuously improve the quality performance of the organizations. This approach continuously improves the performance of healthcare organizations by reducing the number of errors towards patient safety. The present study investigates the influence of Lean Six Sigma on patient safety towards the quality improvement of Malaysian hospitals. This study applied a quantitative research approach and a stratified random sampling method to analyze 16 public and private hospitals. It was used a self-administered survey questionnaire to collect data from 364 respondents. This study developed a research model with four hypotheses. All hypotheses were tested using the Partial Least Squares – Structural Equation Modeling (PLS-SEM) method. The research findings indicate that Lean Six Sigma has a strong positive and significant effect on patient safety and quality improvement of the hospitals. The results of the study also indicate that the Lean Six Sigma method indirectly influences the quality improvement of the hospitals through the mediating effect of patient safety. The present research findings provide a guideline for the practitioners of the healthcare organizations to implement the Lean Six Sigma approach to ensure greater patient safety towards the continuous quality improvement of the healthcare services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".