Day of surgery cancellation rates in urology: Identification of modifiable factors
Bibliographic record
Abstract
OBJECTIVE: Day-of-surgery cancellations have a negative effect on operating room (OR) resources, as well as on patient satisfaction and perception of quality of care. Given increasing wait times in a universal healthcare system and the nature of urological surgery in our aging population, it should be a priority to identify modifiable risks of OR cancellations to assure timely and efficient delivery of care. We explore the rate and reasons for elective surgery cancellations in a Canadian urological practice. METHODS: We evaluated the rate and reason of urological surgery cancellation at a single academic institution, prospectively collected in our centre's Operating Room Scheduling Office System (ORSOS) database. Documented reasons for cancellations were divided into 3 components: (1) structural factors (e.g., no hospital bed); (2) patient factors (e.g., patient unwell); and (3) process factors (e.g., scheduling error). Rates and reasons for cancellations were compared to those of General Surgery and Gynecology. The documented reasons for cancellation in the ORSOS database were confirmed or extended by chart review and interviews with a subset of cancelled patients. RESULTS: Between 2005 and 2009, 1544 out of 19 141 (8.07 %) elective surgical cases were cancelled within the three surgical specialties (general surgery, gynecology and urology); urology had the highest average rate of 9.53%. Non-oncological cases represented a higher percentage of cancelled cases (15%, p < 0.001) and overall rates varied significantly over time in urology compared to the other surgical specialties. Potentially modifiable, process-related causes were by far the most common reason for cancellation (58.5%) and "standby" cases were a common cause of overall cancellation rates. Patient interviews confirmed the emotional and financial impact of cancellation; there was no overwhelming concern that clinical outcomes were negatively affected. CONCLUSIONS: This contemporary exploration of cancelled urological cases is consistent with previous reports, although variable over time and dependent on definitions used. Potentially modifiable, process-related factors appear to be most frequently associated with cancellation, although more thorough and detailed documentation is required to further mitigate inefficient OR use. We suggest that all OR cancellations should be considered to be adverse incidents to be monitored by institutions in a systematic fashion.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".