Decision Support Systems in Anesthesia, Emergency Medicine and Intensive Care Medicine
Notice bibliographique
Résumé
Decision support systems (DSS) in the context of anesthesia, critical care and intensive care medicine consist of several components: the ‘brain’, which integrates a variety of input parameters and delivers as output various informative data which can help the physicians to perform better. They might be considered as ‘automated textbooks’ which by the use of a digitalized infrastructure not only deliver immediately all the necessary health care knowledge to the clinician but also has the capability of intelligently monitoring the actual health status of any given patient – ‘smart monitoring’. It is obvious that in the context of anesthesia, critical – emergency – care and intensive care medicine, such systems are a vital backbone of medical care of the 21st century with a growing number of parameters, growing number of diseases, diagnostic possibilities and treatment options. Decision support systems have the potential to bridge the gap between the theoretical performance of a well trained physicians –who has spent a decade acquiring knowledge and a multitude of manual skills – and his or her actual performance in daily practice. This latter performance can be influenced by any given contextual condition, be it emotional, intellectual or behavioral pattern. What we define as ‘clinical error’ is influenced by several mistakes, one of the most prominent the impossibility to recall all diagnostic and therapeutic options any time for any patient – ‘prospective recall failure’. Computerized information tools have been investigated for more than 2 decades and have been proven to be highly effective in a research environment. However, especially in the specialties which are the object of this chapter, they have not been widely introduced into practice. One of the problems is the clustering of information in modern healthcare facilities between hospital administrators, several laboratory or investigative units and health care providers. The complexity of modern diagnostic and therapeutic options is such that only the most complete coverage of all available patient information can deliver a most complete decision support for the clinician. A lack of standardization, immediate delivery of patient data due to inherent lag times of different working systems, make some of these systems inefficient in reality. Another problem is the ‘user-friendliness’ of these systems – the significant additional time necessary to ‘feed’ the data into the system, as well as the gradual integration of these decision support systems into the daily work pattern: accessibility of the
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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) | 0,001 | 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 ».