The Impact of a Real-Time Locating System within the Perioperative Environment on Physicians and Patients’ Families
Bibliographic record
Abstract
BACKGROUND: Humber River Hospital has implemented a real-time location system (RTLS) within the operating room in order to provide real-time information about patients' status and manage the many components involved during the perioperative journey. OBJECTIVE: The aim of this study was to explore both physicians' and family members' perceptions of the functionality and efficiency of the RTLS within the perioperative environment. METHODS: Semi-structured interviews were conducted with physicians and patients' family members to elicit various perspectives regarding the use of RTLSs throughout the perioperative process. Interviews were recorded and transcribed to extract key themes. RESULTS: Three themes gleaned from physician interviews were system weaknesses, perceptions of potential benefit, and benefits to family members. Three themes uncovered from family member interviews included convenience, ameliorating anxiety, and reducing interruptions. CONCLUSION: Overall, physicians reported that the RTLS had potential to enhance workflow but that significant improvement regarding its implementation and use was needed to reach its full benefit. Family members were unanimous that it provides them with all the tracking information they desire.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".