Improving Operating Room Efficiency in Otolaryngology–Head and Neck Surgery: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: One minute of operating room (OR) time costs $36 to 37. However, ORs are notoriously inefficient. There is growing literature on improving OR efficiency, but no formal review of this topic within otolaryngology has been performed. This study reviews and synthesizes the current literature on improving OR efficiency within otolaryngology. DATA SOURCES: MEDLINE, EMBASE, Web of Science, CINAHL, Cochrane Library, preprints.org, and medRxiv were searched on November 4, 2022. REVIEW METHODS: Published English studies were included if they reported on metrics for improving OR efficiency within otolaryngology. There were no publication date restrictions. Articles were screened by 2 reviewers. Preferred Reporting Items for Systematic Reviews and Meta-analysis reporting for scoping reviews was followed. RESULTS: The search yielded 9316 no-duplicate articles; 129 articles were included. Most of the studies reported on head and neck procedures (n = 52/129). The main tactics included surgical considerations: hemostatic devices, techniques, and team/simultaneous approaches; anesthetic considerations: local anesthetic and laryngeal mask airways; procedure location considerations: procedures outside of the OR and remote technologies; standardization: equipment, checklists, and personnel; scheduling considerations: use of machine learning for booking, considering patient/surgeon factors, and utilizing dedicated OR time/multidisciplinary teams for on-call cases. CONCLUSION: The current literature brings to attention numerous strategies for improving OR efficiency within otolaryngology. Applying these strategies and implementing novel techniques to manage surgical cases may assist in offloading overloaded health care systems and improving access to care while facilitating patient safety and outcomes. Anticipated barriers to implementation include resistance to change, funding, and the current strain on health care systems and providers.
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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 | 0.018 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".