Bibliographic record
Abstract
The increasing waiting times to access healthcare services are a major concern for pa-tients in hospitals. Due to the unpredictability of health issues, hospitals and clinical ser-vices are provided to patients even without prescheduled medical appointments. Unex-pected and random patient arrivals can result in high waiting times. Waiting occurs most-ly because of insufficient resources available compared to demanding service delivery requirements at a given time. Thus, appropriate management of resource scheduling over time can help reduce patient wait times. So far, simulation has mostly been used as a support for strategic decision making in healthcare environments. We are proposing a complementary approach, namely, real-time simulation, to support operational decision making rather than long-term strategic decision making. Real-time simulation is a technique used to get a timely prediction of the system status in a near future (e.g., a few hours). Hospitals can benefit from the capa-bilities of real-time simulations by predicting upcoming bottleneck occurrences in patient care processes and make effective decisions in the present time to avoid undesirable out-comes in the near future. This research presents real-time simulation capabilities for short-term operational decision making of patient care processes in hospitals and the possible ways to run alter-native scenarios and evaluate their results to come up with the most effective solution considering various factors. This thesis also provides tool support based on a leading simulation environment, namely Arena. The tool-supported methodology is evaluated through a realistic cardiac care process in an Ontario community hospital, with encourag-ing results.
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 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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".