Same-day discharge pathway for elective total hip and knee arthroplasty patients: a quality improvement project at a Canadian community hospital
Bibliographic record
Abstract
Total hip arthroplasty (THA) and total knee arthroplasty (TKA) surgeries performed annually are increasing, with over $1.26 billion in hospital costs, according to the 2021/2022 Canadian Institute of Health Information report. A trend towards same-day surgery has helped support the rising demand for arthroplasty in an ageing population and has established evidence for patient safety and satisfaction.Burnaby Hospital sought to develop a same-day pathway to increase at-home recovery opportunities and associated recovery benefits. The aim was to increase the same-day discharge (SDD) rate for THA and TKA from 8% to 15% within a 12-month period.The project team used the Model for Improvement framework to guide the team in achieving the project aim. A series of Plan-Do-Study-Act cycles and ramps were conducted on five interventions: screening tool, focused arthroplasty same-day track automatisation, surgical and anaesthesia standardisation and patient education resources.The health authority's electronic health records (MEDITECH) were used to extract 18 months of baseline data. The data analysis software (SQCPack) was used to monitor the data throughout the project to assess its progress. The results of the SDD rate increased from 8% to 20% with a success rate of 82% SDD, while achieving a decrease in readmission rates to 4-7% from a baseline average of 7-8%. There was no increase in emergency room visits and readmission within 30 days for SDD when compared with the standard inpatient cases. Both staff and patients reported high levels of satisfaction.Driven by a working group creates success with clear goals, strong departmental collaboration, and substantial stakeholder and leadership support. The team viewed failures as learning opportunities to adapt new Plan-Do-Study-Act cycles and strategies for developing continuous improvement throughout the project's life cycle. Process automation was key for a sustainable path for improvements; this provided resiliency against changes from external and staffing pressures.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".