Wait time management strategies at centralized intake system for hip and knee replacement surgery: A need for a blended evidence-based and patient-centered approach
Bibliographic record
Abstract
Objectives: Delays in access to specialty care and elective hip and knee total joint replacement (TJR) surgery remain a major concern among patients with osteoarthritis (OA) in Canada. Centralized intake systems as a wait time management strategy in the face of resource constraints can increase access and patient flow through the system but are not standard practice. We examine how wait time management strategies for the assessment and triaging referrals in a centralized intake system can inform quality improvement initiatives. Design: We developed a discrete-event simulation model using all referrals to the Edmonton Bone and Joint Centre centralized intake system from 2012 to 2016 for the base case model. We assessed the combined effect of three wait time management strategies: improved prioritization, improved sorting through screening, and increased conservative management. Outcomes were measured in terms of patient flow and wait times. Results: The screener sees more patient referrals (7094 compared to 6922), and the number of patients who proceed to surgery is reduced by 282 patients (4%) in the wait time management scenario compared to the base case model. Wait times from referral to surgery are reduced by 54 days for surgical patients. Furthermore, urgent surgical patients experienced lower wait times in all stages of care than non-urgent patients, with wait times from referral to surgery reduced by 86 days. Conclusions: Triaging processes addressing prioritization, screening and conservative management of non-surgical patients can improve patient flow and significantly reduce patient wait times in a centralized intake process for TJR.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".