Abstract P-072: VITALPAD: DESIGNING A MOBILE MONITORING AND COMMUNICATION APPLICATION TO SUPPORT PEDIATRIC INTENSIVE CARE IN A NEWLY DESIGNED ACADEMIC ACUTE CARE CENTRE
Bibliographic record
Abstract
Aims & Objectives: The pediatric intensive care unit (PICU) is a complex environment, in which a multidisciplinary team of clinicians continually observes and evaluates patient information. Data are obtained from multiple, often physically separated devices, and team communication can be challenging.1,2 The VitalPAD is an application designed for mobile display of real-time data from monitoring and therapy devices, and to support team communication. Our previous PICU had an open-plan design, which allowed clinicians to communicate easily, be within earshot of alarms, and maintain continuous eyesight on their patients. The newly-designed PICU features a closed-room layout with patients organized in three pods.We adapted our VitalPAD prototype to this new PICU layout. Methods With research ethics board approval, we conducted ethnographic observations3 of clinicians, rounds, and handovers in the previous unit. VitalPAD was developed using participatory design with clinicians.4 After transition to the new PICU, clinicians were interviewed about communication and monitoring challenges in the new environment. Results Adapted prototype features include an overview showing clinician assignment, patient status, and respiratory support; team communication, and reminder functions. Clinicians reported the design as an intuitive display of vital signs, relevant alerts and reminders, and a user-friendly communication tool. In the new PICU, clinicians reported decreased awareness of overall unit status and of patients not assigned to them. Physicians reported difficulty in monitoring patients when covering multiple pods. Conclusions VitalPAD may facilitate monitoring of patients across pods and more effective communication between clinicians, who are more remote from each other in the new PICU layout. References: [12386492, 23731825, 25843931, ISBN 9780123540515].
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".