Design and implementation of a simulation tool to study wait times in cataract surgery
Bibliographic record
Abstract
Eye cataracts are a common problem for senior people. Long wait times for cataract surgery degrade the patients' quality of life. Reduction in long wait times in eye cataract surgery has got importance as one of the five major priority areas in the health care systems in Canada. The main contribution of this thesis is to design and develop a discrete event simulation tool in JAVA to study the wait times (wait time 1 and wait time 2) for patients in cataract surgical procedure. Two cataract surgical procedures are simulated in the simulation tool Northern Health Cataract Surgical Model (NHCS Model) and Cataract Surgery Generic Model (CSG Model). Two alternative patient referral methods (refer patients to the surgeon with the least number of patients and uniform distribution of patients) are proposed and compared to the existing method to examine which method results in reduced wait times. The impacts of changing the resources (surgeon and OR) on wait times were analysed. The Manitoba Cataract Waiting List Program (MCWLP) priority system is simulated and compared to the existing FCFS policy to see whether the scheduling of patients for surgery based on priority improves wait times. Experimental results show that the two proposed methods significantly reduce wait times. It is found that Northern Health would meet the target wait time 2 (16 weeks) if one more OR (total of two ORs) is allocated for cataract surgery. The use of priority scheduling did not show any improvement in wait time 2. Increasing budget or number of resources is not always easy for any health care authority. This thesis suggests that, if Northern Health authority changes the existing patient referral method, it would definitely reduce wait times for patients.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".