Patient Safety in Pediatrics: a Developing Discipline
Notice bibliographique
Résumé
__Abstract__ \n \nThe publication of the breakthrough report “To Err is Human” by the Institute of Medicine was the \nlaunch of patient safety initiatives all over the world. In the intensive care unit (ICU) of the Erasmus \nMC-Sophia Children’s Hospital this resulted in the institution of a multimodal patient safety management \nsystem under the name Safety First in 2005. This system now includes nine major elements, \nrepresenting monitoring and intervention activities. In this thesis we report on the results and the \nimplementation of the patient safety management system called Safety First. \n \n__Outline of this thesis:__ \nIn part I the concept of patient safety and the Safety First project are introduced. The rationale for \nselecting the elements of the patient safety management system is explained. As preventable mortality \nand morbidity are the public focus as outcome parameters for quality and safety of care, we \nhave studied very long stay patients in our ICU (chapter 2). The goal of this study was to determine \ncharacteristics and mortality in these patients as well as modes of death. Chapter 3 presents an evaluation \nof potentially preventable deaths in our ICU. An important question was whether five years \nof patient safety efforts had resulted in fewer potentially preventable deaths. \nPart II reflects on the difficulties in monitoring adverse events. In chapter 4 we present numbers and \ntypes of adverse events identified with real time physicians’ registration during a 3-month period in \ngeneral pediatric practice. The next chapter is a study into adverse events in the surgical pediatric \nICU in a 2-year period. We combined the physicians’ registration with the Trigger Tool methodology \nas developed by the Institute for Healthcare, Boston, USA. The goals were to determine the rate and \nnature of the adverse events and to compare the two methods. \nIn part III a number of elements of Safety First are described, as well as other studies into patient \nsafety issues relevant to bedside ICU care. Chapter 6 brings the results of critical incident analysis \nwith a focus on the factors contributing to the incident and the resultant recommendations. The \nnext study evaluated the availability and reliability of drug formularies used in our ICU, which are \ncrucial in safe drug prescription. In chapter 8 we discuss the safety of routine MRI scans in preterm \ninfants at 30 weeks gestational age, as reflected by safety incidents and adverse events. In the next \nchapter, safety focused Mortality and Morbidity conference reports were scrutinized for numbers \nand types of recommendations stemming from these meetings. Chapter 10 is a study about nursing \nprotocol violations established with the Critical Nursing Situation Index. \nPart IV describes a study of safety culture in the ICU, as it emerged from a safety attitude questionnaire \nadministered to all staff. We aimed to compare findings to benchmark data and explore any \ndeficiencies. \nIn the general discussion in part V the results of the studies are commented on and future directions \nare given, including guidelines for optimal implementation of a patient safety management system \nand future benchmarking.
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 | 0,027 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,005 | 0,012 |
| Communication savante | 0,017 | 0,009 |
| Science ouverte | 0,002 | 0,009 |
| Intégrité de la recherche | 0,007 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».