Use of multidisciplinary positive deviance seminars to improve efficiency in a high-volume arthroplasty practice: a pilot study
Bibliographic record
Abstract
Background: Positive deviance (PD) seminars, which have shown excellent results in improving the quality of surgical practices, use individual performance feedback to identify team members who outperform their peers; the strategies from those with exemplary performance are used to improve team members’ practices. Our study aimed to use the PD approach with arthroplasty surgeons and nurses to identify multidisciplinary strategies and recommendations to improve operating room (OR) efficiency. Methods: We recruited 5 surgeons who performed high-volume primary arthroplasty and had participated in 4-joint rooms since 2012, and 29 nurses who had participated in 4-joint rooms and in at least 16 cases in our data set. Three 1-hour PD sessions were held in February and March 2021: 1 with surgeons, 1 with nurses, and 1 with both surgeons and nurses to select recommendations for implementation. The sessions were led by a member of the nonorthopedic surgical faculty who was familiar with the subjects discussed and with PD seminars. To determine the success of the recommendations, we compared OR efficiency before and after implementation. We defined success as performance of 4 joint procedures within 8 hours. Results: Eleven recommendations were recorded from the session with nurses and 7 from the session with surgeons, of which 11 were selected for implementation. During the month after implementation, there were great improvements across all time intervals of surgical procedures, with the greatest improvements seen in mean anesthesia preparation time in the room (4.51 min [26.3%]), mean procedure duration (9.75 min [14.0%]) and mean anesthesia finish time (5.78 min [44.0%]) (all p < 0.001). The total time saved per day was 49.84 minutes; this led to a success rate of 69.0%, a relative increase of 73.8% from our 2012–2020 success rate of 39.7% (p < 0.001). Conclusion: The recommendations and increased motivation owing to the individualized feedback reduced time spent per case, allowing more days to finish on time. Positive deviance seminars offer an inexpensive, efficient and collegial means for process improvement in the OR.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".