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Enregistrement W2943369723 · doi:10.1515/sjpain-2018-0128

Lessons learned from piloting a pain assessment program for high frequency emergency department users

2019· article· en· W2943369723 sur OpenAlexaffabout
Rebecca Cherner, John Ecker, Alyssa Louw, Tim Aubry, Patricia A. Poulin, Catherine Smyth

Notice bibliographique

RevueScandinavian Journal of Pain · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueMusculoskeletal pain and rehabilitation
Établissements canadiensOttawa HospitalUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésMedicineEmergency departmentFocus groupDistressPain assessmentHealth carePain managementMedical emergencyFamily medicinePhysical therapyNursing

Résumé

récupéré en direct d'OpenAlex

Background and aims Chronic pain (CP) management presents a challenge for the healthcare system as many individuals experiencing CP cannot access appropriate services. Consequently, individuals may visit emergency departments (EDs) to address their CP, even though this setting is not the most appropriate. CP was identified as a common factor amongst patients with repeat ED use at a hospital in Ottawa, Canada. Thus, staff of the Pain Clinic developed the Rapid Interdisciplinary Pain Assessment Program to improve the care of patients with CP who had a minimum of 12 ED visits in the previous year, who were considered high frequency users (HFUs) of the ED. This evaluation was conducted to guide program improvements in advance of a clinical trial. The results are reported here in order to describe lessons that could be applied to the development of similar programs. The benefits of the program in reducing ED use, pain intensity, disability, and psychological distress have been described elsewhere (Rash JA et al. Pain Res Manag 2018:1875967). Methods Thirty-five people completed semi-structured interviews or a focus group, including eight patients, six ED staff, four primary care physicians (PCP), five Pain Clinic physicians, and 12 program staff members. Questions focused on the program's implementation, as well as strengths and areas for improvement. Results The program was generally consistent in offering the intended patients the services that were planned. Specifically, patients received an interdisciplinary assessment that informed the development of an assessment and treatment plan. These plans were shared with the PCP and added to the patient's hospital electronic medical record. Patients also received education about CP and had access to medical pain management, substance use, and psychological treatments. Inter-professional communication was facilitated by case conferences. Numerous aspects of the program were perceived as helpful, such as the quick access to services, the comprehensive assessment and treatment plans, the individualized treatment, the use of an interdisciplinary approach to care, and the communication and relationships with other care providers. However, challenges arose with respect to the coordination of services, the addition of supplementary services, the accessibility of the program, patients' perceptions of being misunderstood, communication, and relationship-building with other service providers. Conclusions The program sought to improve the care of HFUs with CP and reduce their ED use for CP management. The program had numerous strengths, but also encountered challenges. Developers of programs for HFUs with CP are encouraged to consider these challenges and suggested solutions. These programs have the potential to deliver effective healthcare to individuals with CP and reduce ED overutilization. Implications Our findings suggest that the following points should be considered in the development of similar programs: the inclusion of a case manager; the use of strategies to ensure inter-professional communication, such as secure electronic consultation, approaches to allow access to assessment and treatment plans, and offering a range of modalities for communication; holding regular case conferences; determining the appropriate types of services to offer; and working to address patients' fears of being labelled.

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)0,005
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 enseignants0,351
Score d'incertitude au seuil0,638

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
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)0,0010,000

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 Opus0,031
Tête enseignante GPT0,353
Écart entre enseignants0,322 · 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

Citations2
Publié2019
Routes d'admission2
Résumé présentoui

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Même revueScandinavian Journal of PainMême sujetMusculoskeletal pain and rehabilitationTravaux en français237 207