Cancellation of elective surgery: rates, reasons and effect on patient satisfaction
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: 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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