Optimizing Quality of Hospital Services and Inpatient Satisfaction through Lean Principles
Bibliographic record
Abstract
Aim: The aim of this study is to evaluate the impact of Lean principles on improving hospital service quality and inpatient satisfaction tertiary care hospital in Pune, India. It focuses on reducing key inefficiencies to enhance patient experiences and operational efficiency. Information & Methods: This quantitative study was conducted at tertiary care hospital in Pune, India, with 110 inpatients who had been admitted for at least three days. Data were collected through a closed-ended questionnaire based on Lean’s seven waste categories. Methods: A cross-sectional design was used, and data were analyzed using SPSS version 20. Descriptive statistics summarized demographics and survey responses, while chi-square tests and multivariate logistic regression assessed the relationships between Lean variables and inpatient satisfaction. Findings: The findings reveal that Lean principles significantly improved hospital service quality and inpatient satisfaction. Reductions in "waiting" and "motion" wastes were strongly correlated with higher patient satisfaction, highlighting the importance of streamlined processes and reduced wait times. Efficient inventory management also emerged as a key factor in enhancing satisfaction, while "excess processing" and "overproduction" showed less influence on patient satisfaction, indicating areas for further improvement. These results underscore the effectiveness of Lean in optimizing healthcare delivery and improving patient experiences. Conclusion: The study concludes that Lean principles effectively improve hospital service quality and inpatient satisfaction by reducing key wastes such as waiting, motion, and inventory inefficiencies. Continuous implementation of Lean practices can lead to more efficient and patient-centered healthcare delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".