Lean transformation to reduce costs in healthcare: A public hospital case in Turkey
Bibliographic record
Abstract
Until recently, hospitals have adapted a series of quality standards to improve the quality of their services. However, these standards do not provide any cost reduction that is worth mentioning within which qualified personnel, material and indirect costs are already very high. To overcome this problem, lean production techniques have been integrated to the healthcare processes in the last few years. This study aimed to reduce costs by both improving and shortening the time of Social Security Instituition (SSI) based invoicing processes of a public hospital in Bursa, Turkey during the period of 2013-2014. To accomplish this goal various lean tools such as value stream mapping, poka-yoke, kaizen and standardization were presented and implemented. For validation purposes, montly SSI cuts, total income and process times before and after the lean transformation were analysed and compared. Furthermore, patient satisfaction surveys were carried out in order to investigate the negative effects of cost oriented lean studies. Results reveal that a clear improvement in total income is achievable, while maintaining the patient satisfaction levels. In this case, an eight percent improvement in SSI cuts resulting in a clear improvement on total income values has been obtained. Findings indicate that implementing lean tools on hospital processes is an effective way to reduce the healthcare costs while maintaining the patient satisfaction levels. The results of this study will stimulate the research in adopting lean techniques not only for patient satisfaction, but also for cost reduction purposes in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".