Assessing the electronic Bedside Paediatric Early Warning System: A simulation study on decision-making and usability
Notice bibliographique
Résumé
BACKGROUND: The Bedside Paediatric Early Warning System (BedsidePEWS) is a clinical decision support tool designed to augment clinician expertise, objectively identify children at risk for clinical deterioration, and standardize and prioritize care to improve outcomes in community settings. Although the paper-based BedsidePEWS documentation record has been shown to improve clinicians' perception of their ability to detect deterioration and follow care recommendations, research is needed to asses this impact empirically. Furthermore, as hospitals progressively move toward electronic clinical systems, knowledge regarding the impact of BedsidePEWS' novel electronic interface on clinicians' performance and user experience is required. OBJECTIVES: The primary objectives of this study were (1) to compare adherence to evidence-based care recommendations using a) electronic health record software, b) paper BedsidePEWS, and c) a novel electronic BedsidePEWS interface, and (2) to describe end-users' experiences of usability and opportunities for improvement of both paper and electronic BedsidePEWS. METHODS: Paediatric nurses participated in a repeated measures simulation study. Participants assessed simulated patients, documented patient data, and responded to a series of questions regarding follow-up care for each patient. Three patient types (i.e., stable, mild deterioration, severe deterioration) were assessed in each of three intervention conditions (i.e., electronic health record, paper BedsidePEWS, electronic BedsidePEWS). Following simulation scenarios, participants provided comments regarding the usability of the paper and electronic tools. RESULTS: Participants made 12.7% and 18.0% more appropriate care decisions with paper and electronic BedsidePEWS, respectively, than with the electronic health record intervention (p < 0.001). Accurate BedsidePEWS severity of illness score calculation was related to better adherence to evidence-based care recommendations (65%), compared to inaccurate calculation (55%), and electronic BedsidePEWS was associated with 15.7% fewer calculation errors than paper (p < 0.005). Electronic BedsidePEWS demonstrated usability benefits over its paper predecessor, including automatic score calculation and data plotting, and the potential to eliminate double charting, and participants expressed a preference for electronic BedsidePEWS in all aspects of the debrief questionnaire (p < 0.001). CONCLUSIONS: BedsidePEWS in both paper and electronic formats significantly improved participants' ability to detect deterioration and follow care recommendations compared to electronic health record software. Furthermore, results suggest that electronic BedsidePEWS would afford improved patient care in excess of the paper-based original and further contribute to the standardization, prioritization, and improvement of care in community settings.
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Comment cette classification a été obtenuedéplier
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 | 0,002 | 0,001 |
| 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 | 0,000 | 0,000 |
| 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 ».