Lean Healthcare Improvement Model Using Simulation-Based Lean Six-Sigma and TRIZ
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As a part of primary care clinic, the Indonesian-community health center is responsible for efforts to encourage independence and create a community for healthy living. The service facility commonly used is the general polyclinic. A number of problems occur are non-value added activities that lead to a longer waiting time. Therefore, the aim of this study is to improve the service performance at Indonesian-community health center. This research used six sigma DMAIC model in evaluating the current service system using value stream mapping (VSM), determining critical waste using the Borda count method, identifying the root causes of critical waste, designing the alternative service system improvements using theory of inventive problem solving (TRIZ), building alternative simulation models using Flexsim software, and evaluate the improvement plan. The result shows that the average time of general polyclinic services in current system is 107 minutes with waiting as critical waste (23%). There are two health-service improvement scenarios developed using theory of inventive problem-solving method (TRIZ), i.e. scenario 1 and scenario 2. Both scenarios are evaluated by considering some criterias, i.e. idle time, waiting time, number of patients served, lead-time and process cycle efficiency. The best scenario is scenario 2 with 48.2% reduction in lead time and process cycle efficiency increased by 48%.
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.
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it