Impact of Electronic Data on the Development of Care in Critically Ill Children
Notice bibliographique
Résumé
Major changes are occurring in pediatric intensive care due to the transition from paper to electronic clinical data collection. In this supplement of the Journal of Pediatric Intensive Care , a panel of experts review the literature and report their experience on the progress of this revolution. The electronic platform allows for data to be collected in a systematic way. Matton et al[ 1 ] report the customized implementation of a paperless pediatric intensive care electronic medical record (EMR). They used a 20-month preparation period and a living laboratory approach after the “go-live” (continuous monitoring of issues and problems and rapid intervention to correct them). Their report focuses on safety issues and staff satisfaction, both of which are major challenges during such transitions. Storage of electronic clinical data can contribute to improved quality of medical care and can facilitate clinical research. Such databases can help describe practice variation, allow for hypothesis generation, test for feasibility of clinical trials, and can perform comparative effectiveness research. Several examples that demonstrate this can be evoked. Wetzel[ 2 ] used the vast experience acquired from Virtual Pediatric Systems (VPSs) to describe the registry design needed to improve quality of care. Khemani[ 3 ] used the example of pediatric acute respiratory distress syndrome and reported on the major issues that arose when anyone plans to aggregate several databases, to improve knowledge and to provide preliminary data for research on rare diseases. As soon as patient data are electronically available, they can be organized and processed in a manner that allows for the use of this clinical knowledge to enhance clinical decision making. This area of innovation refers to the creation of clinical decision support systems (CDSSs). A CDSS can deliver timely general clinical knowledge and guidance, intelligently processed patient data, or a combination of both. Information delivery formats can include data and order entry facilitators, filtered data displays, reference information, and alerts. At the bedside, CDSS can be used to improve compliance to guidelines and protocols. Sward and Newth[ 4 ] reviewed CDSS for mechanical ventilation in children that are designed to make mechanical ventilation management safer, more consistent, and more lung protective. Fartoumi et al[ 5 ] reviewed a CDSS created to minimize secondary brain injury after traumatic brain injury, which helps clinicians quickly analyze and respond to ongoing clinical changes, thus optimizing patient status and guiding management. Adams and Longhurst[ 6 ] created a CDSS for pediatric blood product prescriptions and reported its impact on our adherence to evidence-based red blood cell and plasma transfusion practices. Zaglam et al[ 7 ] reviewed CDSSs for lung disease diagnosis on chest radiographs in intensive care; this was justified because interpretation of chest radiographs is difficult due to the lack of a standardized interpretation. Dynamic databases that collect prospectively synchronized patient data at a high frequency rate (< 0.1Hz) from various medical devices (monitoring systems, ventilators, infusion pumps, extracorporeal circulation, …) can estimate patient physiologic behaviors. Brossier et al[ 8 ] reported the clinical and teaching interest of cardiorespiratory physiology modeling systems (i.e., virtual patients) and studied the methodologies that can be used to validate such virtual patients using dynamic databases (also referred to as perpetual patients ). All the manuscripts in this supplement reflect the tremendous efforts that are currently being made to bring to the bedside a maximum of useful knowledge that is integrated into the workflow and that will ultimately improve the management of critically ill children. Note Philippe Jouvet received funding from the Fonds de recherche en Santé du Québec, Ministère de la Santé et des Services Sociaux du Québec and Sainte-Justine Hospital.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,096 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,013 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».