Application of a physical science model in the analysis of patient flow in a hospital
Bibliographic record
Abstract
One of the most relevant aspects in hospital management relies on how to properly control and predict the patient flow, that is, the paths and the time sequence a whole set of patients run in their journey inside the hospital, as they look for treatment. This issue is of the utmost importance since it interferes in the quality of the healthcare delivered to a person and also has a huge impact on both the costs for the patient and the operational costs for the hospital. This work intends to collaborate with the comprehension of the patient flow analysis and to offer a mathematical model analogous to a physical model capable of, qualitatively at first sight, describing the main variables and properties of this flow. We also present the logical elements that allow the manager to develop quantitative flow evaluations adaptable to a specific institution, based on local measurements of the variables described here. This theoretical formulation can directly be applied to practical situations concerning the management of patient flow. The relevant variables and their mathematical relations can be used by the manager in order to quantify each relevant patient circuit in a hospital. This way, it is expected that recurring problems derived from the unwanted variations in the patient flow can be anticipated and corrected by the manager.
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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 | 0.004 |
| 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 | 0.002 |
| 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".