Using patient flow simulation to improve access at a multidisciplinary sleep centre
Bibliographic record
Abstract
The lack of timely access to diagnosis and treatment for sleep disorders is well described, but little attention has been paid to understanding how multiple system constraints contribute to long waiting times. The objectives of this study were to identify system constraints leading to long waiting times at a multidisciplinary sleep centre, and to use patient flow simulation modelling to test solutions that could improve access. Discrete-event simulation models of patient flow were constructed using historical data from 150 patients referred to the sleep centre, and used to both examine reasons for access delays and to test alternative system configurations that were predicted by administrators to reduce waiting times. Four possible solutions were modelled and compared with baseline, including addition of capacity to different areas at the sleep centre and elimination of prioritization by urgency. Within the model, adding physician capacity improved time from patient referral to initial physician appointment, but worsened time from polysomnography requisition to test completion, and had no effect on time from patient referral to treatment initiation. Adding respiratory therapist did not improve model performance compared with baseline. Eliminating triage prioritization worsened time to physician assessment and treatment initiation for urgent patients without improving waiting times overall. This study demonstrates that discrete-event simulation can identify multiple constraints in access-limited healthcare systems and allow suggested solutions to be tested before implementation. The model of this sleep centre predicted that investments in capacity expansion proposed by administrators would not reduce the time to a clinically meaningful patient outcome.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".