Delay Cascade in Queueing Network of Cardiovascular Care
Bibliographic record
Abstract
A cardiovascular care system can be regarded as a kind of queueing network, for units which own queues are con-nected to each other according to their temporal relation-ship (e.g., since a patient should register before he receiving the consultation in a hospital, there is a directed link from the appointment unit to the consultation unit). Previous re-searches about shortening long queues or long wait times often focus on some intuitionally impact factors, like limited resources, unpredictable patient behaviors and inefficient management policies in an isolated unit (e.g., emergency department), seldom consider the factors concealed among units in a queueing network. This paper will figure out that delay cascade, a small delay in one place resulted in long delays elsewhere, is an important factor which may leads to the covariance fluctuation of wait times among connected units. In this paper, we investigate delay cascade, or how de-lays disseminate from one unit to another in a cardiovas-cular care queueing network. Prior to a further investi-gation of the dynamic patterns (e.g., how does the delay cascade happen) in cardiovascular care, we 1) first iden-tify whether two connected units have a wait time relation-ship by Structure Equation Modeling based on empirical data of Ontario, Canada; 2) in order to explain the under-line mechanisms accounting for such kind of wait time rela-tionship, we develop a series Markovian queueing network model to analyze the relationship of delay cascade and wait time mathematically. Our simulation results show that the delays in a unit will cascade within its own queue, as well as spread to other connected units, so that the total delays and wait times in the whole system will be more heavily. 1
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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 | 0.008 |
| 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 | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".