Cancellation of elective surgery: rates, reasons and effect on patient satisfaction
Bibliographic record
Abstract
Background: The cancellation of elective surgeries is a major problem that increases wait times, exacerbates costs and can negatively affect patients, both psychologically and physically. Our objectives were to investigate the reasons for cancellations across specialties at a single centre, to compare these reasons with previous data from the same centre between 2005 and 2009 and to examine how cancellations affected patients' lives and views of the medical system in cases when the cancellations were potentially preventable. Methods: Cancellation records of all elective surgeries scheduled between June 1, 2012, and Jan. 31, 2016, at a medium-sized, tertiary care, academic centre were retrospectively reviewed. We evaluated the rates and reasons for cancellation and interviewed a subset of patients whose surgery was cancelled for a potentially preventable reason (i.e., operating room running late, bed shortage, emergency case took place of scheduled surgery). Results: Across 11 surgical specialties, 2933 of 20 881 surgeries (14.0%) were cancelled and of these, 2448 (83.5%) were for administrative or structural reasons. Compared with the data collected previously for general, gynecological and urological procedures, cancellation rates increased from 8.1% to 11.8%. Although patients reported inconvenience, they were generally satisfied with the availability and the quality of the health care they received. Conclusion: Consistent with the previous study, our data suggest that most cancellations occur because of administrative or structural processes that are potentially preventable. Targeting these processes may help to reduce cancellations for elective surgeries and thereby improve economic efficiency and patient outcomes.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".