Evaluating the Effectiveness of Smart Glasses in Reducing Patient Care Time in Emergency Departments: Cohort Study From the Hangzhou Asian Games
Bibliographic record
Abstract
Background: Challenges in emergency medicine include overcrowding, insufficient emergency care resources, and extended emergency department (ED) waiting times. These issues contribute to delays in treatment and unfavorable outcomes. This situation worsens in events with large crowds and particularly worsened during the COVID-19 pandemic. The integration of augmented reality (AR) smart glasses could potentially enhance patient care in the ED. Objective: This study aims to assess the effectiveness of AR smart glasses in reducing patient care time in the ED during the 19th Asian Games and the Fourth Asian Para Games Hangzhou 2022 (HAG2022). The study specifically compares the prepreparation time (PPT) and consult response time (CRT) in patients receiving teleconsultations via AR smart glasses versus those receiving standard care without AR. Methods: This retrospective study was conducted between September 13, 2023, and October 28, 2023, during HAG2022. The data were gathered from AR smart glasses using 5G technology at the HAG2022 village and electronic health records at Sir Run Run Shaw Hospital, China. The study included 2 groups: the teleconsultation by augmented reality telemedicine system (ARTS) group and the non-ARTS group. The main data assessed were PPT and CRT in ED. Results: During the research period, 80 patients were divided into 2 cohorts: the ARTS cohort (n=10) and the non-ARTS cohort (n=70). Gender and age demographics showed no significant differences between the cohorts. The ARTS cohort had a significantly lower average PPT of 23 minutes compared to the non-ARTS cohort's 40.3 minutes (P<.001). In addition, CRT in the ARTS cohort was significantly lower at 15.6 minutes compared to the non-ARTS cohort's 164.8 minutes (P=.03). The outcomes suggest that smart glasses are effective in decreasing PPT and CRT. Conclusions: AR smart glasses have the potential to enhance patient admission efficiency and reduce care time in EDs. However, despite these benefits, further research is needed to confirm their effectiveness, and additional studies are essential to identify the challenges and barriers to their successful implementation in emergency medicine.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".