Development of a Clinical Interface for a Novel Newborn Resuscitation Device: Human Factors Approach to Understanding Cognitive User Requirements
Bibliographic record
Abstract
BACKGROUND: A novel medical device has been developed to address an unmet need of standardizing and facilitating heart rate recording during neonatal resuscitation. In a time-critical emergency resuscitation, where failure can mean death of an infant, it is vital that clinicians are provided with information in a timely, precise, and clear manner to capacitate appropriate decision making. This new technology provides a hands-free, wireless heart rate monitoring solution that easily fits the clinical pathway and procedure for neonatal resuscitation. OBJECTIVE: This study aimed to understand the requirements of the interface design for a new device by using a human factors approach. This approach combined a traditional user-centered design approach with an applied cognitive task analysis to understand the tasks involved, the cognitive requirements, and the potential for error during a neonatal resuscitation scenario. METHODS: Fourteen clinical staff were involved in producing the final design requirements. Two pediatric doctors supported the development of a visual representation of the activities associated with neonatal resuscitation. This design was used to develop a scenario-based workshop. Two workshops were carried out in parallel and involved three pediatric doctors, three neonatal nurses, two advance neonatal practitioners, and four midwives. Both groups came together at the end to reflect on the findings from the separate sessions. RESULTS: The outputs of this study have provided a comprehensive description of information requirements during neonatal resuscitation and enabled product developers to understand the preferred requirements of the user interface design for the device. The study raised three key areas for the designers to consider, which had not previously been highlighted: (1) interface layout and information priority, as heart rate should be central and occupy two-thirds of the screen; (2) size and portability, to enable positioning of the product local to the baby's head and allow visibility from all angles; and (3) auditory feedback, to support visual information on heart rate rhythm and reliability of the trace with an early alert for intervention while avoiding parental distress. CONCLUSIONS: This study demonstrates the application of human factors and the applied cognitive task analysis method, which identified previously unidentified user requirements. This methodology provides a useful approach to aid development of the clinical interface for medical devices.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".