Ecological Interface Design in Neuro-Critical Care
Notice bibliographique
Résumé
Neuro-critical care is a data-intensive environment that requires physicians to integrate information \nacross multiple screens, sources, and software. Despite the advances in neuromonitoring techniques, \ninterfaces that allow for viewing and analyzing of historic data are not common. However, historical \ndata is critical to identify patterns important for patient care. Instead, physicians view the trends of a \npatient’s neurophysiological variables by continuously watching the bedside monitor or they rely on \nchecking the paper (or digital) charts for a patient where variables have been recorded periodically \n(usually once an hour). In neuro-critical care, physicians need to understand the historic and current \nstate as well as predict the future state of intracranial pressure (ICP). ICP is the most monitored brain-specific physiologic variable in the Intensive Care Unit (ICU) and is considered a biomarker for \nsecondary brain injury. As a result, ICP would benefit greatly from showing key patterns important to \npatient state and care. \n \nThe ICU is a stressful, dynamic, and time-sensitive environment where the performance of physicians \nand their ability to correctly diagnose and manage patient treatment has a significant impact on \npatient outcomes. Physicians rely on the bedside physiologic monitor to detect changes in physiologic \nvariables. The monitor must provide the information required to understand the patient’s condition so \nphysicians can determine the optimal treatment plan. With the high cognitive demands and complex \nsociotechnical environment of the ICU, an opportunity exists for improved neuro-critical care \nmonitoring to support physicians’ decision-making. Ecological Interface Design (EID) is an approach \nto interface design that has proven effective for complex, sociotechnical, real-time, and dynamic \nsystems. Research suggests that an EID approach combined with user-centered design has a positive \nimpact on performance, especially in unfamiliar scenarios. \n \nThe objective of this research is to explore an EID design approach combined with user-centered \ndesign to enhance the bedside physiologic monitor through the addition of visualizations that help \nsupport physicians' understanding of complex relationships and concepts in neuro-critical care. The \nhope is that providing more-advanced visualizations on the bedside physiologic monitor will lead to \nimproved situation awareness, decreased mental workload, and expertise development acceleration of \nnovice clinicians in the neuro-ICU. \n \nThe work presented in this thesis builds on the Cognitive Work Analysis (CWA) and observations in \nthe ICU already completed by Uereten et al (2020). The design of the visualizations for use on the \nbedside physiologic monitor was highly iterative and involved the inputs from the CWA and \nobservations as well as ongoing feedback and focus areas provided by Dr. Victoria McCredie, our \nclinical collaborator and critical care physician at Toronto Western Hospital. The visualizations were \nevaluated and validated in semi-structured interviews with trainees (fellows) and experts (staff \nphysicians) in neuro-critical care. The semi-structured interviews with trainees were used as a \npreliminary usability assessment of the visualizations and the interviews with staff physicians were \nused to iterate and refine the designs. The results from both sets of interviews were used to create a \nfinal design prototype that is currently being tested in a usability study with trainee physicians \n(January-March 2023).
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».