Rapid implementation of an outpatient arthroplasty care pathway: a COVID-19-driven quality improvement initiative
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it