Applications and User Perceptions of Smart Glasses in Emergency Medical Services: Semistructured Interview Study
Bibliographic record
Abstract
BACKGROUND: Smart glasses have been gaining momentum as a novel technology because of their advantages in enabling hands-free operation and see-what-I-see remote consultation. Researchers have primarily evaluated this technology in hospital settings; however, limited research has investigated its application in prehospital operations. OBJECTIVE: The aim of this study is to understand the potential of smart glasses to support the work practices of prehospital providers, such as emergency medical services (EMS) personnel. METHODS: We conducted semistructured interviews with 13 EMS providers recruited from 4 hospital-based EMS agencies in an urban area in the east coast region of the United States. The interview questions covered EMS workflow, challenges encountered, technology needs, and users' perceptions of smart glasses in supporting daily EMS work. During the interviews, we demonstrated a system prototype to elicit more accurate and comprehensive insights regarding smart glasses. Interviews were transcribed verbatim and analyzed using the open coding technique. RESULTS: We identified four potential application areas for smart glasses in EMS: enhancing teleconsultation between distributed prehospital and hospital providers, semiautomating patient data collection and documentation in real time, supporting decision-making and situation awareness, and augmenting quality assurance and training. Compared with the built-in touch pad, voice commands and hand gestures were indicated as the most preferred and suitable interaction mechanisms. EMS providers expressed positive attitudes toward using smart glasses during prehospital encounters. However, several potential barriers and user concerns need to be considered and addressed before implementing and deploying smart glasses in EMS practice. They are related to hardware limitations, human factors, reliability, workflow, interoperability, and privacy. CONCLUSIONS: Smart glasses can be a suitable technological means for supporting EMS work. We conclude this paper by discussing several design considerations for realizing the full potential of this hands-free technology.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".