Use of video education in post-operative patient counselling: A quality improvement initiative
Bibliographic record
Abstract
INTRODUCTION: This quality improvement study examined if a video-based resource could reduce delayed discharges after robotic prostatectomy while maintaining high levels of patient satisfaction. METHODS: From April 2018 to February 2020, all patients undergoing robotic-assisted radical prostatectomy (RARP) were asked to complete an anonymous survey evaluating their perioperative experience. The quality improvement (QI) intervention started in March 2019 with a series of six educational videos being shown to all patients. The videos were used to supplement postoperative instruction. The discharge times of all patients were obtained from The Ottawa Hospital Data Repositories. A run chart analysis was used to detect change in discharge time (outcome measure). Patient satisfaction (balancing measure) was analyzed using Chi-squared analysis and descriptive statistics. RESULTS: A total of 425 robotic prostatectomies (199 pre-intervention, 226 post-intervention) were available. Analysis of the run chart revealed non-random change favoring earlier discharge in the intervention group (p<0.05), with a pre-intervention late discharge rate of 64% and a post-intervention late discharge rate of 55%. A total of 140 surveys (59 pre-intervention, 81 post-intervention) assessing patient satisfaction were completed, corresponding with a response rate of 29.6% and 35.8%, respectively. Median score on a 10-point scale for overall satisfaction was equal between the intervention and non-intervention groups (9 [interquartile range (IQR 8-10) vs. 10 [IQR 8-10], p=0.92). CONCLUSIONS: Patient satisfaction with care and education was high for all patients and was not negatively impacted by this intervention. Video education tools may be one method to help improve the discharge process following RARP.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".