Improving safety for children with cardiac disease
Notice bibliographique
Résumé
The complexity of the modern systems providing health care presents a unique challenge in delivering care of the required quality in a safe environment. Issues of safety have been thrust into the limelight because of adverse events highly publicized in the general media. In the United States of America, improving the safety and quality in health care has been set forth as a priority for improvements in the 21st century in the report from the Institute of Medicine. Many measures have now been initiated for improving the safety of patients at hospital, regional, and national level, and through initiatives sponsored by governments and private organizations. In this review, we summarize known concepts and current issues on the safety of patients, and their applicability to children with congenital cardiac disease. Prior to examining the issues of medical error and safety, it is important to define the terminology. An error is defined as the failure of a planned action to be completed as intended, also known as an execution error, or the use of a wrong plan to achieve an aim, this representing a planning error. An active error is an error that occurs at the level of the frontline operator, and the effects of which are felt immediately. A latent error is an error in the design, organization, training and maintenance, that leads to operator errors, and the effects of which are typically dormant in the system for lengthy periods of time. Latent errors may cause harm given the right circumstances and environment. An adverse event is defined as an injury resulting from medical intervention. A preventable adverse event is an adverse event that occurs due to medical error. Negligent adverse events are a subset of preventable adverse events where the care provided did not meet the standard of care expected of that practitioner. The study of improving the delivery of safe care for our patients is a rapidly growing field. Important components for development of programmes to improve the safety of patients include the leadership for the programme, the implementation of process design based on human limitations, the promotion of teamwork and function, the anticipation of unexpected events, and the creation of a learning environment. Much is yet to be learned about the risk and incidence of adverse events during hospitalization of children with congenital cardiac disease. Errors due to human factors, such as poor communication, poor coordination, and suboptimal team work, have shown to be important causes of adverse outcomes in children undergoing cardiac surgery, and should be a focus for improvement. Future research on evaluating causes and prevention of medical errors and adverse events in this population at high risk, and consuming high resources, is essential. Issues of inadequate safeguards for patients have been prominent in the media, and have been highlighted in reports from the Institute of Medicine. Our review discusses research on the causes of medical error, and proposes concepts to design successful programmes to improve safety for the patients on a local level.
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 | 0,002 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».