Notice bibliographique
Résumé
Every surgeon has begun his or her career as a medical undergraduate with a keen awareness of the basis for our assessments in the domains of knowledge, skills and attitudes. It is this last domain in which psychological and human factors are crucial to good outcomes of medical performance. In this issue of the Canadian Journal of Surgery (page 22), Mark Fleming and colleagues1 describe how they investigated the interpersonal competencies of cardiac surgery teams as a means to identify team members' attitudes toward teamwork. This is a most timely report, because it tackles some of the fundamental reasons for medical errors such as those reported in the Canadian Adverse Events Study.2 Using the Operating Room Management Attitudes Questionnaire (ORMAQ) to study cardiac surgery team members, including surgeons, nurses, perfusionists, anesthesiologists and residents, the investigators demonstrated inherent group differences based upon factors such as seniority and occupational group membership. Respondents in the study reported that the most frequent types of error included miscommunication, performing actions at the incorrect time and failing to follow established procedures. If we hypothesize that patient safety can be improved by optimizing team skills in the areas of communication, leadership and cooperation, surgeons should promptly adopt new practices. Indeed, recent studies show that insufficient communication is a contributing factor in 60%–80% of adverse events in medicine.3 In order to implement improved team performance, some key steps can be taken in our departments to improve team training, provide audits of team performance and pursue new opportunities for funding of improved patient safety. Improved team training in medical education is an obvious place to start. Surgical teams share many domains with aviation where the concepts of „Crew Resource Management” have been developed to utilize clinical data, equipment and resources toward an ultimate goal of safe management. Several examples of such training are familiar to surgeons including trauma team training according to Advanced Trauma Life Support (ATLS) principles, emergency department triage training simulation and several examples of safety protocols for general anesthesia. The principles of crew training and development hinge upon the qualities of leadership, „followership,” communication and cooperation. Using these principles, Ostergaard and associates implemented team training in medical education in Denmark.4 Training is done using the anchoring principle of simulation together with learning objectives, educational tools and evaluation. There is no doubt that full-scale simulation training improves outcomes for learners, but skeptics will ask if this approach also improves patient outcomes and increases safety for patients. Intuitively, this makes a great deal of sense, although there are few data to support this statement. Nevertheless, senior surgical team members should encourage this type of development. Of interest in their study, Fleming and colleagues1 report that compliance with rules, procedures and policies was described as deficient by one-third of respondents. This finding is in accord with our own experience with the recent implementation of a policy for correct side and site marking. Audits in our experience reveal that up to 40% of the time the „time out” part of the policy (in which the surgical team verifies verbally that the correct patient, correct operation and correct side are being treated just before the incision is made) is not followed. More than one of us has even been ridiculed for conducting the activity! Should we address this failure to follow policy correctly with a „carrot or a stick” approach? Perhaps as a regular part of feedback to the surgical team, we need to use enabling technology as a way of reducing medical error. For example, some of our own operating rooms are now equipped with video surveillance that during an operative procedure can confidentially record events simultaneously related to the patient's vital signs and to the activities of the operating surgeons, anesthesiologists, trainees and operating room nurses. With multipanel simultaneous recording in these domains, the entire team could receive and review direct feedback on ways to improve adherence to safety protocols. Some centres have already started this approach for minimally invasive surgery (Dr. Gerald Fried, McGill University, Montreal, Que.: personal communication, 2005). Such technology could help us add sophistication to the tired „morbidity and mortality” rounds format that is currently used in many institutions. A final comment pertains to the pursuit of knowledge of systems to improve patient safety through team-based training. Responding to reports of adverse outcomes in patients,2 some federal and provincial ministries have expressed interest in supporting research to improve patient safety. For example, a chair in patient safety research has recently been established through partnership between the British Columbia Ministry of Health and the Departments of Anesthesia and of Pharmacology and Therapeutics at the University of British Columbia. Such a research initiative rightfully extends across multiple medical departments including anesthesia and surgery. These initiatives will prove essential to show a clear relation between team-based training and improved patient outcomes. This will also inform change in our medical education curricula particularly as our new teams of resident trainees, nursing staff and health professionals mature in their future career practices. In summary, Fleming and associates1 have given us some crucial insight into cardiac surgery team behaviour that has far-reaching implications for patient outcomes and future research. The challenge for surgeons is to become involved in evaluation strategies to assess our own performance in team skills. After all, every surgeon has a vested interest in teamwork that leads to safer patient outcomes! Garth L. Warnock, MD Coeditor
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 | 0,011 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,010 |
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 ».