Smart Glasses for Caring Situations in Complex Care Environments: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Anesthesia departments and intensive care units represent two advanced, high-tech, and complex care environments. Health care in those environments involves different types of technology to provide safe, high-quality care. Smart glasses have previously been used in different health care settings and have been suggested to assist health care professionals in numerous areas. However, smart glasses in the complex contexts of anesthesia care and intensive care are new and innovative. An overview of existing research related to these contexts is needed before implementing smart glasses into complex care environments. OBJECTIVE: The aim of this study was to highlight potential benefits and limitations with health care professionals' use of smart glasses in situations occurring in complex care environments. METHODS: A scoping review with six steps was conducted to fulfill the objective. Database searches were conducted in PubMed and Scopus; original articles about health care professionals' use of smart glasses in complex care environments and/or situations occurring in those environments were included. The searches yielded a total of 20 articles that were included in the review. RESULTS: Three categories were created during the qualitative content analysis: (1) smart glasses as a versatile tool that offers opportunities and challenges, (2) smart glasses entail positive and negative impacts on health care professionals, and (3) smart glasses' quality of use provides facilities and leaves room for improvement. Smart glasses were found to be both a helpful tool and a hindrance in caring situations that might occur in complex care environments. This review provides an increased understanding about different situations where smart glasses might be used by health care professionals in clinical practice in anesthesia care and intensive care; however, research about smart glasses in clinical complex care environments is limited. CONCLUSIONS: Thoughtful implementation and improved hardware are needed to meet health care professionals' needs. New technology brings challenges; more research is required to elucidate how smart glasses affect patient safety, health care professionals, and quality of care in complex care environments.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".