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Enregistrement W4285089743 · doi:10.29173/cjen182

Improving assessments and follow-up for pediatric emergency department mental health visits

2022· article· en· W4285089743 sur OpenAlexvenueaboutno aff
Teresa Lightbody, Jennifer Thull‐Freedman, Stephen B. Freedman, Nicole Finseth, Stephanie McConnell, Angela Coulombe, Jennifer Woods, Shelley Groves-Johnston, Bruce Wright, Matthew M. Morrissette, Amanda S. Newton

Notice bibliographique

RevueCanadian Journal of Emergency Nursing · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueEmergency and Acute Care Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEmergency departmentTriageGeneral partnershipMental healthMedicineQuality managementMedical emergencyBest practiceHealth carePatient safetyNursingFamily medicineOperations managementBusinessPsychiatryPolitical scienceEngineeringManagement system

Résumé

récupéré en direct d'OpenAlex

Background: Over the past decade, the number of children presenting to emergency departments (ED) with mental health (MH) concerns has increased substantially. EDs struggle to respond to this increase with approaches that comprehensively address patient needs. The lack of standardized processes to perform risk stratification, assess severity, and ensure access to follow-up care pose barriers to the provision of safe MH care. Our team addressed this gap by introducing an evidence-based care bundle to Alberta’s two pediatric EDs. This report presents the quality improvement (QI) approach used to ensure fidelity of implementation at one of the EDs. This initiative was funded by Alberta Innovates (Partnership for Research and Innovation in the Health System; PRIHS). Methods: We used the Model for Improvement to test and implement each bundle element: suicide risk screening (Ask Suicide-Screening Questions [ASQ]) at ED triage; a tool (HEADS-ED) to streamline and standardize MH assessments by ED-based MH nurse); and an urgent, single-session ‘Choice Appointment’ with a MH professional within 96 hours of the ED visit for patients lacking access to appropriate and timely MH follow-up care. The two ED-based bundle elements did not require additional resources or funding and are expected to reduce length of stay. The follow-up clinic option for ED patients without resources is intended to prevent crisis escalation and match patients with supports. Each new practice was introduced sequentially over a 2-week period. For each practice, we identified 1 to 2 improvement aims, developed key driver diagrams, and selected primary outcomes and measures. Each practice was implemented using Plan-Do-Study-Act (PDSA) cycles with initial tests of change starting small and becoming larger as learning accrued from previous cycles. Our QI team included families with lived experience, patient care and unit managers, nurse educators, frontline healthcare providers, content experts, and clinical leaders who supported staff and led change management strategies. A nurse was hired as a QI lead to support execution of PDSA cycles. We developed a sustainability plan which included embedding education regarding new practices in new healthcare staff orientation, having a measurement strategy to ensure that improvement was maintained, and planning for transition of responsibility for these processes to operational and medical leadership. Evaluation Methods: Primary aims included: 80% of targeted patients would receive the ASQ and HEADS-ED and 100% of children eligible for an Urgent, single-session ‘Choice Appointments’ would be offered it within 96 hours. We used clinical data from the electronic health record (Epic/Connect Care) as well as patient experience data collected via parent/caregiver surveys to determine if the aims for each practice were achieved. We included balancing measures to test whether changes in care in one part of the system introduced unintended consequences in other parts. We evaluated results for the primary aims using run charts to rapidly detect change according to established rules for detecting special cause. We discussed the results from each PDSA cycle in the context of existing healthcare resources to support implementation of each element of the bundle. Results: Tests of change to introduce suicide risk screening began February 1st, 2021. Performance was measured in weekly intervals. The median initial use of ASQ by triage nurses was with 77% of MH patients (686/901 patients), and over time, improved to 93% (319/350 patients), with special cause (shift) in noted September 2021. Tests of change to introduce the HEADS-ED tool began February 16th, 2021. Initial use of the HEADS-ED by MH nurses was 81% (440/555) and improved to 87% (201/227) with special cause (shift) noted August 2021. Urgent, single-session ‘Choice Appointments’ were offered to all patients who did not have timely and access to urgent follow-up with an existing mental healthcare provider with 89.1% having an appointment booked within 96 hours of the ED visit (139/156). Advice and Lessons Learned: Three plans were viewed as crucial to the success of this initiative: 1) a robust strategy to develop proposed changes based on best evidence combined with patient and staff engagement; 2) a comprehensive QI strategy to test, measure, and implement changes; and 3) regular communication and collaboration among ED staff, mental healthcare staff, patients/families, and hospital leadership. There were also lessons learned regarding what could have further enhanced project success: 1) enhanced communication strategies using multiple methods to ensure that project communications reached all stakeholders, including those not regularly present in the ED; 2) hiring the QI lead earlier to begin change management prior to bundle implementation; and 3) outlining a transition plan for clinical data management and bundle monitoring earlier to ease the QI transition to clinical leadership.

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)0,016
score de la tête « metaresearch » (Gemma)0,052
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,136
Score d'incertitude au seuil0,269

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0160,052
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0020,001
Communication savante0,0020,002
Science ouverte0,0030,004
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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 Opus0,030
Tête enseignante GPT0,347
Écart entre enseignants0,317 · 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
Publié2022
Routes d'admission2
Résumé présentoui

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Même revueCanadian Journal of Emergency Nursing→Même sujetEmergency and Acute Care Studies→Travaux en français237 207→