Information Display in the Intensive Care Unit – Considerations for System Design and Implementation
Bibliographic record
Abstract
Healthcare working environments are complex, and intensive care units (ICUs) are particularly complex due to the influx of data to the healthcare professionals who are providing continuous care to the most critically ill patients. Systems that are designed to work in these environments should take into consideration varied patient conditions, the clinical professionals who use these systems, and the features and performance requirements that will support their efforts to provide care to their patients. We suggest that developing systems that will meet these challenges requires customized design approach, including cognitive system engineering. Until recently, this work domain has been largely ignored by manufacturers of patient monitoring systems. This panel brought together two separate teams who have been using such an approach independently to design new systems for information integration and display in ICU settings. The goals of this panel discussion were to take a close look at the tools and methods that are being used for such a cognitive system engineering approach to the design processes, and to review the recommendations and concepts that are emerging from these processes from each of the two independent teams. This paper summarizes the presentations made during the panel by the two teams regarding updates of ongoing work followed by a lively discussion between panelists and the symposium participants in the audience. Each team had its unique design process that was customized to the specific target ICU, the available resources and goals. The designed systems have original features that evolve from the unique needs of the target unit, yet the designs also share some common features.
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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 | 0.091 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".