Novel Interface Designs for Patient Monitoring Applications in Critical Care Medicine: Human Factors Review
Bibliographic record
Abstract
BACKGROUND: The patient monitor (PM) is one of the most commonly used medical devices in hospitals worldwide. PMs are used to monitor patients' vital signs in a wide variety of patient care settings, especially in critical care settings, such as intensive care units. An interesting observation is that the design of PMs has not significantly changed over the past 2 decades, with the layout and structure of PMs more or less unchanged, with incremental changes in design being made rather than transformational changes. Thus, we believe it well-timed to review the design of novel PM interfaces, with particular reference to usability and human factors. OBJECTIVE: This paper aims to review innovations in PM design proposed by researchers and explore how clinicians responded to these design changes. METHODS: A literature search of relevant databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, identified 16 related studies. A detailed description of the interface design and an analysis of each novel PM were carried out, including a detailed analysis of the structure of the different user interfaces, to inform future PM design. The test methodologies used to evaluate the different designs are also presented. RESULTS: Most of the studies included in this review identified some level of improvement in the clinician's performance when using a novel display in comparison with the traditional PM. For instance, from the 16 reviewed studies, 12 studies identified an improvement in the detection and response times, and 10 studies identified an improvement in the accuracy or treatment efficiency. This indicates that novel displays have the potential to improve the clinical performance of nurses and doctors. However, the outcomes of some of these studies are weakened because of methodological deficiencies. These deficiencies are discussed in detail in this study. CONCLUSIONS: More careful study design is warranted to investigate the user experience and usability of future novel PMs for real time vital sign monitoring, to establish whether or not they could be used successfully in critical care. A series of recommendations on how future novel PM designs and evaluations can be enhanced are provided.
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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 | 0.020 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".