A Taxonomy of Distraction Mitigation Strategies for Operating Rooms
Bibliographic record
Abstract
Operating rooms (ORs) are complex dynamic settings that require individual OR team members to conduct focused and cognitively demanding tasks. The team focus may be disrupted by distractions such as alarms, phone calls, or in-person communications irrelevant to work. Some of these distractions can result in a break in task performance which are known as interruptions (Healey, Primus, & Koutantji, 2007). Distractions are inherent in the ORs and are generally considered as harmful (Mentis, Chellali, Manser, Cao, & Schwaitzberg, 2016). Studies have shown that distractions are associated with disrupted surgical flow (Wiegmann, Elbardissi, Dearani, Daly, & Sundt III, 2007), decreased cognitive task performance (Hsu, Man, Gizicki, Feldman, & Fried, 2008), increased time taken to complete surgical tasks, impaired dexterity, and increased surgical error rates (Ahmed, Ahmad, Stewart, Francis, & Bhatti, 2015). Individuals can experience prospective memory failures after an interruption, i.e., forgetting to resume the interrupted task or resuming the task at an incorrect step (Reason & Hobbs, 2003). Negative effects of distractions have been the central concern of these studies; however, literature is increasingly agreeing on the potential benefits of distractions (e.g., communication) such as decreasing stress, boredom, and mental fatigue (Weigl, Antoniadis, Chiapponi, Bruns, & Sevdalis, 2015) and improving surgical task performance (Kyrillos & Caissie, 2017). It is important to foster positive distractions, while mitigating the negative ones. Research is limited when explaining the conditions in which OR distractions are beneficial or harmful and the consequences of removing or reducing these distractions. Despite this lack of evidence, several techniques, mostly adapted from other high risk industries such as aviation, have been suggested for mitigating OR distractions (see Feil, 2014 for a comprehansive review). We created a taxonomy to categorize OR distraction mitigation strategies that we identified through literature reviews and discussions with OR teams from our partner hospital. Systems approach in human error management expects the system to prevent errors from occurring or propagating by having several defense barriers (Reason, 2008). Hence, our taxonomy sorts mitigation strategies that act as defense barriers using three dimensions: technology type (hard, soft), management control type (feedforward, concurrent, feedback), and target (individual, team, hospital). Hard technology refers to strategies that have tangible forms (e.g., physical tools such as checklists) while soft technology refers to the ones that have intangible forms (e.g., training and policies). Feedforward control aims to mitigate potential distractions by putting in system defenses based on anticipation of OR distractions that may happen. Concurrent control introduces defenses that are active during surgery. Feedback control compares surgical events and performance with desired outcomes and provides feedback and corrective control. Finally, distraction mitigation strategies should not only target individuals but also the OR team and the hospital at large, therefore, the last dimension captures this perspective. Thorough examinations are needed before implementing different mitigation strategies since these strategies, both hard and soft, can become sources of distraction themselves if designed, implemented, or maintained poorly. A safety culture needs to be adopted and supported, which can be achieved through the inclusion of feedforward, concurrent, and feedback control mechanisms for managing OR distraction. Success of distraction mitigation efforts requires active participation from individuals, OR teams, and hospital management. This systematic approach in categorizing mitigation strategies is aimed to assist OR distraction management and to improve patient safety together with work conditions for OR team members. However, further research is needed to assess the effectiveness of different strategies in practice. Acknowledgments. Funding for this work was provided by NSERC and the Centre for Healthcare Engineering of the University of Toronto, Faculty of Applied Science and Engineering.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".