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Enregistrement W3183322518 · doi:10.29173/cjen130

An analysis of individual and department triage variances to identify, quantify, and improve markers of triage nurse accuracy

2021· article· en· W3183322518 sur OpenAlexaffvenueabout
Rebecca Cotton, Richard H. Drew, Matthew J. Douma, Domhnall O’Dochartaigh, Candice Keddie, Karen Muncaster, Christopher Picard

Notice bibliographique

RevueCanadian Journal of Emergency Nursing · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueEmergency and Acute Care Studies
Établissements canadiensCovenant HealthAlberta Health Services
Organismes subventionnairesnon disponible
Mots-clésTriageEmergency departmentStaffingAuditMedicineMedical emergencyNursingManagement

Résumé

récupéré en direct d'OpenAlex

An analysis of individual and department triage variances to identify, quantify, and improve markers of nurse triage accuracy. Rebecca Cotton, Richard Drew, Matthew Douma, Domhnall O’Dochartaigh, Candice Keddie, Karen Muncaster, Christopher Picard Background: Canadian Emergency Departments (ED) use the five-point Canadian Triage Acuity Scale (CTAS) to sort and prioritize patients according to acuity. CTAS scores are used to make decisions on patient flow, staffing complement, and funding. Despite this, there is a paucity of literature describing how CTAS data can be audited, and how the data can inform quality improvement/assurance (QI/QA). Implementation: Triage data downloaded from Tableau were analyzed using Microsoft Excel and IBM SPSS 26. Staff were informed of the audit using email and social media, and invited to discuss the results with educators and administrators. Staff identified for intervention were approached individually with the administrative plan. Anonymized versions of the work plan were posted on the departmental audit board. Nurses triaging greater than 50% department average were offered the option to triage less frequently, while nurses triaging less than 50% the department average were preferentially placed in triage. Nurses triaging fewer than 100 patients per year were informed they would be relieved of triage responsibility unless their rates increased above threshold. Nurses “down-triaging” patients at rates greater than 2 SD were informed that if their practice remained outside 2 SD at repeat audit they would be relieved of triage responsibility until they voluntarily completed CTAS refresher training. Nurses with average assigned CTAS scores > 2 SD department average had 20 visits randomly audited per month for error/appropriateness. Evaluation Method: Computer-assisted analysis of complete triage records was conducted for August 2019 to August 2020 at the Misericordia Hospital Emergency. Complete triage entries of every patient triaged by all triage trained nurses in the department were examined. Nurse’s with practice variation two deviations from department mean were identified and received additional detailed audits. Items examined for error were: FTE adjusted triage frequency; average CTAS score assigned; triage score manual override “down/up-triage” rate; proportion of absent Numeric Pain Scores (NPS) for patients with primary presenting complaints of pain; and vital signs modifier error rates. Initial department averages were used for benchmarking individual nurses; zone averages were used to benchmark department performance. Nurses were interviewed, audit results and action plans were posted. Repeat audits were performed on a three-month basis and benchmarked to initial measures, and a staff awareness campaign was enacted to improve NPS scoring. Data were extracted using text-parsing algorithms programmed into Microsoft Excel and analyzed using IBM SPSS 26. Data were normally distributed and descriptive statistics were calculated using means and standard deviations. T-testing was used for comparisons, and all testing was two-tailed with a pre-defined significance set at 0.05. Results: After the 3rd quarterly audit and associated interventions, global improvements were appreciated in triage nurse practice. There was a 68% reduction in the need for administrative action (n=51, n=18) with reduced variance in individual nurse triage rates and a 50% reduction in nurses who triaged >50% more patients than their peers. 50% fewer nurses had a mean triage rate >.02 above or below department average, there was an 86% reduction in high risk vital sign error rates, a 78% reduction in ”down-triage” rates, and a 6.5% improvement in documentation of numerical pain scores. Advice and Lessons Learned:1) Triage data analytics can rapidly identify staff with significant deviations from the average,making auditing and QI/QA activities more efficient and effective. 2) Having a concrete performance management framework and dissemination plan in place areessential for auditing to have a significant impact on triage consistency and quality over time. 3) Future QI/QA work should consider expanding computer-assisted text parsing to identifypatients at risk for mis-triage for reasons other than vital sign derangement, which will allowfor broader ED rollout across the Edmonton Zone and beyond.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,347
Score d'incertitude au seuil0,666

Scores Codex et Gemma par catégorie

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

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.

Tête enseignante Opus0,036
Tête enseignante GPT0,376
Écart entre enseignants0,340 · 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 tête enseignante, 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

Citations6
Publié2021
Routes d'admission3
Résumé présentoui

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Même revueCanadian Journal of Emergency NursingMême sujetEmergency and Acute Care StudiesTravaux en français237 207