Lean management for improving hospital waiting times—Case study of a Vietnamese public/general hospital emergency department
Bibliographic record
Abstract
INTRODUCTION: Emergency departments (EDs) at public hospitals in Vietnam typically face problems with overcrowding, as well as being populated by a wide variety of illnesses, resulting in increasing dissatisfaction from patients. To alleviate these problems, we used the increasingly popular value-stream mapping (VSM) and lean strategy approaches to (1) evaluate the current patient flow in EDs; (2) identify and eliminate the non-valued-added components; and (3) modify the existing process in order to improve waiting times. METHODS: Data from a total of 742 patients who presented at the ED of 108 Military Central Hospital in Hanoi, Vietnam, were collected. A VSM was developed where improvement possibilities were identified and attempts to eliminate non-value-added activities were made. A range of issues that were considered as a resource waste were highlighted, which led to a re-design process focusing on prioritizing blood tests and ultrasound procedures. On the administrative side, various measures were considered, including streamlining communication with medical departments, using QR codes for healthcare insurance payments, and efficient management of X-ray and CT scan online results. RESULTS: By implementing a lean approach, the following reductions in delay and waiting time were incurred: (1) pre-operative test results (for patients requiring medical procedures/operations) by 33.3% (from 134.4 to 89.4 min); (2) vascular interventions by 10.4% (from 54.6 to 48.9 min); and (3) admission to other hospital departments by 49.5% (from 118.3 to 59.8 min). Additionally, prior to the implementation of the lean strategy approach, only 22.9% of patients or their proxies (family members or friends), who responded to the survey, expressed satisfaction with the ED services. This percentage increased to 76.5% following the curtailment of non-value-added activities. Through statistical inferential test analyses, it can be confidently concluded that applying lean strategy and tools can improve patient flow in public/general hospital EDs and achieve better staff coordination within the various clinical and administrative hospital departments. To the authors' knowledge, such analysis in a Vietnamese hospital's ED context has not been previously undertaken.
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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 | 0.003 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".