A Far-View Intensive Care Unit Monitoring Display Enables Faster Triage
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Bibliographic record
Abstract
Although nurses perform the majority of the clinical tasks in an intensive care unit, current patient monitors were not designed to support a nurse's workflow. Nurses constantly triage patients, deciding which patient is currently in the most need of care. To make this decision, nurses must observe the patient's vital signs and therapeutic device information from multiple sources. To obtain this information, they often have to enter the patient's room. This study addresses 3 hypotheses. Information provided by far-view monitoring displays (1) reduces the amount of time to determine which patient needs care first, (2) increases the accuracy of assigning priority to the right patient, and (3) reduces nurses mental workload. We developed 2 far-view displays to be read from a distance of 3 to 5 m without entering the patient's room. Both display vital signs, trends, alarms, infusion pump status, and therapy support indicators. To evaluate the displays, nurses were asked to use the displays to decide which of 2 patients required their attention first. They made 60 decisions: 20 with each far-view display and 20 decisions with a standard patient monitor next to an infusion pump. Sixteen nurses (median age of 27.5 years with 2.75 years of experience) participated in the study. Using the 2 far-view displays, nurses more accurately and rapidly identified stable patients and syringe pumps that were nearly empty. Median decision times were 11.3 and 12.4 seconds for the 2 far-view displays and 17.2 seconds for the control display. The 2 far-view displays reduced median decision-making times by 4.8 to 5.9 seconds, increased accuracy in assignment of priority in 2 of 7 patient conditions, and reduced nurses' frustration with the triaging task. In a clinical setting, the proposed far-view display might reduce nurses' mental workload and thereby increase patient safety.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it