Bedside Critical Care Staff Use of Intensive Care Unit Telemedicine: Comparisons by Intensive Care Unit Complexity
Bibliographic record
Abstract
BACKGROUND: Effects of Intensive Care Unit (ICU) telemedicine on patient and staff outcomes are mixed. Variation in utilization is potentially driving these differences. INTRODUCTION: ICU telemedicine utilization is understudied, with existing research focusing on telemedicine staff. We assess ICU telemedicine utilization from the perspective of the end user-ICU staff-to better understand how telemedicine use is conceptualized and practiced at the bedside. MATERIALS AND METHODS: We conducted a thematic content analysis of semistructured interviews with bedside ICU staff. Staff were interviewed at seven ICUs in six Veterans Health Administration facilities, representing varying ICU complexities and points in time (2 and 12 months postimplementation of ICU telemedicine). RESULTS: Fifty-eight bedside ICU staff described instances of telemedicine use, which were categorized into three types: Urgent ICU Patient Care, Clinical Decision-Making and Support, and General ICU Patient Care. The most commonly described use was General ICU Patient Care and the least common was Urgent ICU Patient Care. ICU staff from lower complexity ICUs had fewer descriptions of use compared to staff at higher complexity ICUs. At 12 months postimplementation, staff recounted more instances of all three utilization types. DISCUSSION: It is important to understand how telemedicine is being used within ICUs to evaluate its impact. The presence of three types of use, variability in use by ICU complexity, and change in use over time suggest the need for comprehensive measures of utilization to evaluate effectiveness. CONCLUSIONS: ICU telemedicine needs to develop an agreed upon typology for documenting ICU telemedicine utilization and incorporate these measures into models of its effect on clinical outcomes.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".