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Enregistrement W3034066879 · doi:10.29173/cjen59

Emergency nurse use of ultrasound guidance for vein cannulation: a three site quality improvement initiative and registry

2020· article· en· W3034066879 sur OpenAlexaffvenueabout
Domhnall O Dochartaigh, Warren Ma, Christopher Picard, Richard H. Drew, Matthew J. Douma

Notice bibliographique

RevueCanadian Journal of Emergency Nursing · 2020
Typearticle
Langueen
DomaineHealth Professions
ThématiqueCentral Venous Catheters and Hemodialysis
Établissements canadiensUniversity of AlbertaMisericordia Community HospitalCovenant HealthAlberta Health Services
Organismes subventionnairesnon disponible
Mots-clésMedicineQuality managementUltrasoundQuality (philosophy)Medical emergencyEmergency departmentEmergency medicineRadiologyNursingOperations managementEngineering

Résumé

récupéré en direct d'OpenAlex

Background AHS suggests a limit of four attempts at traditional peripheral vascular access, however there are limited current options at many sites for these patients. Between 10 and 25 percent of patients present to the emergency department (ED) with difficult to cannulate veins. In these patients ultrasound guided catheter placement decreases the number of IV attempts, decreases time to successful IV placement, improves patient satisfaction, and in adult patients decreases central line use. Emergency nurses have been shown to successfully employ ultrasound-guided peripheral vascular access. Physician and Nursing clinical practice guidelines place a high recommendation for this practice. Despite the evidence and recommendations, in Canadian EDs, with notable exceptions there remains minimal standard procedural uptake or ED research. Implementation For difficult peripheral intravenous access a standardized ultrasound guided nurse performed procedure was implemented in 2016 at the University of Alberta (UAH) ED, in 2017 to the Royal Alexandra Hospital (RAH) ED, and in 2018 the Misericordia Community Hospital (MCH) ED. An education module was created that included didactic learning and an exam, approximately one hour of in-person training which included vessel and structure identification and cannulation practice on a gel model until competence was achieved, and finally three successful mentored starts prior to independent practice. Mentorship ensured good technique was followed, provided additional tips to improve practice, and most importantly ensured an IV attempt was on a patient with veins amenable to a novice ultrasound provider attempt (e.g. if a patient was assessed to be a challenging ultrasound start with limited vein options the mentor would place the IV in much the same way as traditional IV placement mentoring). The ultrasound technique taught was a single operator, short access or traverse approach with dynamic tip tracking where the catheter needle tip is continually visualized as the target vessel is cannulated. Catheter placement is confirmed with the catheter tip visualized intraluminal and with an ultrasound visualized saline flush. This study reports on the first 30 nurses trained at the UAH, 12 at the RAH and 6 at the MCH. Evaluation Methods A quality improvement (QI) registry documented complications and was used to improve education, training, and procedural success. The two QI study objectives were 1) to determine ultrasound program success for all sites by comparing QI results to historic results from other programs 2) to determine if an abbreviated training regimen (shorter than previously documented for adult patients in Canada) can be used to train nurses in EDs with minimal support or pre-existing experience with UGIVC. Staff who had achieved independent practice voluntarily completed a tracking form whenever an ultrasound procedure occurred. Completed forms were assessed on a continual basis for any opportunities for improvement. Qualitative feedback was also obtained from informal interviews, a focus group, and a survey of the newly trained nurses. Feedback was thematically analyzed and grouped into themes for reporting. Data and trends from the registry were used to reinforce education to promote greater procedural success. Also identified were questions to add to the tracking form to improve the usefulness of the registry. Ongoing review will identify if these efforts improve practice. Opportunities for system improvements were managed through consultation with all stake holders including nursing management, CNEs, physicians, and bedside nurses. Program evaluation will shape all aspects of the program development. Results At the UAH, RAH, and MCH respectively; the mean number of failed IV attempts [SD] before UGIV was: 4.2 [2.5]; 3.4 [2.1]; 4.77 [2.9]; while first pass success by novice provider (1-10 UGIV starts) was 76%; 66%; and 62%. Success increased rapidly with the number of starts and plateaued after 100. Complications occurred in 4/374 (1%) starts. Qualitative feedback suggests that provider and patient positioning, and equipment preparation improve individual success; engaged staff and a QI registry improve program success; even in cases with more reported pain, patients prefer UGIV to traditional placement. Advice and Lessons Learned Creating an ultrasound guided peripheral IV program and quality registry that supports emergency nurse use of this procedure is possible. First pass and overall catheter success rates and low reported compilications are reassuring. The quality registry has provided useful data to support practice and suggest modifications to the education and site specific system level supports provided. An example of system feedback is that newly trained staff need to have a clinical assignment that allows the opportnuity to utilize the procedure. Also enough mentors are required to support new staff. A third interesting system issue identifed is the possible effects of the training on traditional difficult IV placement skill and how to best support this. Emergency physicians and nurse champions can play a key supportive role to ensure the success of the program.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,017
score de la tête « metaresearch » (Gemma)0,029
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,131
Score d'incertitude au seuil0,260

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0170,029
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,006
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0020,004
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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.

Tête enseignante Opus0,151
Tête enseignante GPT0,402
Écart entre enseignants0,250 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2020
Routes d'admission3
Résumé présentoui

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