Integration of value stream mapping and agent‐based modeling for OR improvement
Bibliographic record
Abstract
Purpose The purpose of this paper is to present the research for improvement of the operating room in a hospital, to reduce patient waiting time and increase the resource utilization. Design/methodology/approach Value stream mapping (VSM) is used to represent the entire operating room (OR) process and patient flow to identify problems. Agent‐based simulation (ABS) is applied to model human behaviors in the OR operation. Agents perform human factors in the simulation model with autonomous and interactive functions actively. Findings The research outcomes prove the effectiveness of integrated VSM and ABS to improve decision making in human‐centred healthcare environments. Research limitations/implications Because the state is dynamically changed, the task priority needs to be updated dynamically. The nurse schedule to transport patients between different units is to be detailed. Practical implications Long waiting lists in hospitals lead to patient dissatisfaction and care quality reduction. It is crucial to identify inefficiency and to improve the healthcare delivery effectively. Originality/value The paper shows how VSM and ABS are integrated in the modeling for the dynamic OR planning. It improves the simulation modeling of healthcare delivery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".