Evaluating a Shared Decision Support Tool for Pediatric Cardiopulmonary Arrest: Mixed Methods Usability Study
Bibliographic record
Abstract
Background: Effective team communication is critical in pediatric cardiopulmonary arrest management, where delays or miscommunication can jeopardize survival. TeamScreen, a web-based interface displayed on a large screen, was developed to enhance cardiopulmonary resuscitation (CPR) by providing real-time visualization of clinical data and resuscitation steps aligned with the American Heart Association pediatric advanced life support algorithms. Objective: This study evaluated the usability of the TeamScreen Figma prototype, evaluating how efficiently and accurately experienced emergency physicians and nurses retrieved critical information during a simulated pediatric in-hospital cardiac arrest scenario. Although no strict time constraints were imposed, participants were instructed to perform the tasks as spontaneously and as quickly as possible. Methods: Usability testing involved 20 pediatric emergency physicians and nurses with varied CPR experience. Participants performed 21 information retrieval tasks within a simulated pediatric cardiac arrest scenario (shockable rhythm). The data collected included audio-video recordings via the think-aloud method and participant responses to the Post-Study System Usability Questionnaire (PSSUQ) version 3 and a posttest survey. Effectiveness, efficiency, and satisfaction were measured by task completion rates, time-on-task metrics, and PSSUQ scores, respectively. Think-aloud data were analyzed for usability issues using Nielsen Norman Group's rating scale and Bastien and Scapin's ergonomic criteria. Results: Five physicians and 15 nurses achieved a mean task success rate of 81.19% (SD 16.87%), with a mean completion time of 8.13 (SD 7.07) seconds, calculated across all 21 tasks and all participants. PSSUQ scores reflected high satisfaction (mean 2.40 [SD 1.24] of 7.00; the lower the better), notably for information clarity and system utility. Qualitative analyses identified 16 usability issues, of which 5 were deemed major, primarily involving information visibility, navigation, and density, highlighting areas for interface and workflow enhancement. Conclusions: The usability evaluation confirmed TeamScreen's potential to improve real-time information access during pediatric CPR, with high task success and satisfaction scores supporting its role in aiding decision-making. Challenges with visibility, navigation, and information density require further refinement. These findings will guide improvements and inform the design of multicenter trials to assess TeamScreen's efficacy in simulation-based resuscitation settings.
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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 | 0.042 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".