A systematic literature review on the utilization of extended operating room hours to reduce surgical backlogs
Bibliographic record
Abstract
This article is part of the Research Topic 'Health Systems Recovery in the Context of COVID-19 and Protracted Conflict'. Introduction: Hospital managers address elective patient surgical backlogs with different strategies: increasing installed capacity, managing demand and improving efficiency. Recently, and particularly since the COVID-19 elective surgery suspension, extended operating room hours has been used to reduce waiting lists by taking advantage of empty operating rooms and existing surgical teams. Methods: Two research questions are raised: (1) which are the scientific literature's insights related to the use of extended operating room hours to help reduce surgery backlogs? and (2) provided that a hospital decides to extend its operating room opening time, what are the main challenges and the key aspects to consider in the design and implementation of policies to manage extended operating room hours? A systematic review on Web of Science database was conducted to gather existing literature, published from January 2012 to December 2021, regarding strategies to reduce waiting lists using empty operating rooms outside regular working hours. Results: A total of 12 papers were selected as relevant to address the two research questions. Results were organized according to their main features, namely setting, type of strategy, methodology, and how human resources are handled. Discussion: The review suggests that extended operating room hours might be problematic if current staff is used and that a careful choice of patients should be made. However, its potential to reduce waiting times and its implications are discussed only superficially. Therefore, we analyze the implications of extending operating room hours from four different perspectives (management, staff, patients, and strategy deployment) and define some recommendations for policy makers and healthcare managers when implementing it in practice.
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How this classification was reachedexpand
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 | 0.017 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".