The Use of Simulation in Support of Managerial Decision Making in Hospitals
Bibliographic record
Abstract
Executive decision-making for health care organizations’ managers is a process typically based on the manager’s experience and various patient data the organization serves. These decisions that executive managers make, affect the quality of care and efficiency metrics that organizations are required to maintain or improve based on standards established by the Ministry of Health. Among the factors affecting the quality of care and efficiency are various material and human resources, as well as diagnosis and treatment complexity and occurrence of nosocomial infections. Extreme events may also occur which may require additional material and human resources. Supporting the decision-making process with simulation applications may offer managers the ability to understand better the effect of their decisions and compare different outputs. While the simulations cannot replace human experience and educated intuition, it can help augment the decision process and could lead to better outcomes for patients and more efficient use of material and human resources. Lastly, simulations could allow managers to make adequate substitutions when resources are scarce without compromising the quality of care and with greater consideration of the impact of work schedule on health care workers’ burnout and exhaustion.
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.
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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".