Do You See What I See? Insights from Using Google Glass for Disaster Telemedicine Triage
Bibliographic record
Abstract
INTRODUCTION: Disasters are high-stakes, low-frequency events. Telemedicine may offer a useful adjunct for paramedics performing disaster triage. The objective of this study was to determine the feasibility of telemedicine in disaster triage, and to determine whether telemedicine has an effect on the accuracy of triage or the time needed to perform triage. METHODS: This is a feasibility study in which an intervention team of two paramedics used the mobile device Google Glass (Google Inc; Mountain View, California USA) to communicate with an off-site physician disaster expert. The paramedic team triaged simulated disaster victims at the triennial drill of a commercial airport. The simulated victims had preassigned expected triage levels. The physician had an audio-video interface with the paramedic team and was able to observe the victims remotely. A control team of two paramedics performed disaster triage in the usual fashion. Both teams used the SMART Triage System (TSG Associates LLP; Halifax, England), which assigns patients into Red, Yellow, Green, and Black triage categories. The paramedics were video recorded, and their time required to triage was logged. It was determined whether the intervention team and the control team varied regarding accuracy of triage. Finally, the amount of time the intervention team needed to triage patients when telemedicine was used was compared to when that team did not use telemedicine. RESULTS: The two teams triaged the same 20 patients. There was no significant difference between the two groups in overall triage accuracy (85.7% for the intervention group vs 75.9% for the control group; P = .39). Two patients were triaged with telemedicine. For the intervention group, there was a significant difference in time to triage patients with telemedicine versus those without telemedicine (35.5 seconds; 95% CI, 72.5-143.5 vs 18.5 seconds; 95% CI, 13.4-23.6; P = .041). CONCLUSION: There was no increase in triage accuracy when paramedics evaluating disaster victims used telemedicine, and telemedicine required more time than conventional triage. There are a number of obstacles to available technology that, if overcome, might improve the utility of telemedicine in disaster response.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".