Rapid implementation of an outpatient arthroplasty care pathway: a COVID-19-driven quality improvement initiative
Bibliographic record
Abstract
BACKGROUND: Hip and knee total joint arthroplasty (TJA) procedures are two of the most common inpatient surgical procedures worldwide. Outpatient TJA has emerged as a feasible option. COVID-19 caused significant constraints on inpatient surgical resources and contributed to a growing surgical backlog. We present a quality improvement (QI) initiative aimed at adding an outpatient TJA pathway to our pre-existing inpatient TJA programme, with the target of performing 25% of our primary TJA as outpatients. METHODS: This was a QI study at a tertiary level arthroplasty centre. To achieve our aim, a patient-centred needs analysis revealed the need to develop patient selection criteria, perform a specific and tailored anaesthetic, provide patient education and conduct virtual care follow-up. Based on these findings, an outpatient TJA intervention bundle was developed and implemented. RESULTS: After implementing the outpatient pathway, 65 patients were scheduled for outpatient TJA. Fifty-five (84.6%) patients were successfully discharged home on the day of surgery. Successful outpatient TJA accounted for 33.3% of all primary TJAs performed at our intuition throughout the study period. There was excellent adherence to the intervention protocols, with the success hinging on multidisciplinary team and supported QI culture. Thirty-day emergency department visits for inpatient and outpatient TJAs were 8.93% and 6.15%, respectively. No outpatient TJA patients required hospital readmission within 30 days. CONCLUSION: Our study demonstrates that implementation of an outpatient TJA pathway in response to inpatient resource constraints during the COVID-19 pandemic is feasible. The findings of this report will be of interest to surgical centres facing surgical backlog and constraints on inpatient resources during and after the pandemic.
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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".