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Enregistrement W3033685164 · doi:10.29173/cjen52

A multidisciplinary approach to treating low back pain in the ED: improving patient outcomes

2020· article· en· W3033685164 sur OpenAlexaffvenue
Lesley C. Beique, Jason Martyn

Notice bibliographique

RevueCanadian Journal of Emergency Nursing · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueMusculoskeletal pain and rehabilitation
Établissements canadiensAlberta Health Services
Organismes subventionnairesnon disponible
Mots-clésMultidisciplinary approachMedicinePhysical therapyLow back painAlternative medicineSociology

Résumé

récupéré en direct d'OpenAlex

Background: In collaboration with emergency physicians, the physiotherapy and pharmacy teams at RGH implemented a novel multidisciplinary, evidence-based pathway, addressing a significant care gap in the management of low back pain (LBP) in urban emergency departments (EDs). To accomplish this, the physiotherapist conducts a neuromusculoskeletal exam, prior to the physician. They provide treatment including manual therapy, mobilization, education, home-exercises and referral to community resources. The pharmacist then reviews medications, discusses pain management, prescribes analgesia and creates a plan for outpatient analgesia. This occurs while the patient awaits the physician, avoiding increases to length of stay (LOS) and reducing burden on physicians. Implementation: At minimum, a site wishing to implement the pathway requires a team consisting of a physiotherapist (PT) and pharmacist dedicated to the ED, called the Rapid Assessment Back Team (RABT). To operationalize the RABT successfully, the selected PT and pharmacist must be confident practitioners and have a solid understanding of LBP, red-flags, and appropriate treatment. To avoid increasing LOS, patients are seen during the 2-hour average waiting time required to see a physician. The project team consisted of physiotherapists, pharmacists, nurses, physicians, managers, and QI leaders, formed to facilitate a collaborative approach to implementation. The Prosci® ADKAR model and Plan-Do-Study-Act (PDSA) cycles were used to implement the pathway and troubleshoot operational challenges. Evaluation Methods: Front-line staff manually collected data on response time, treatments, adverse events, and resources provided. The investigators reviewed patient charts to record opioid prescriptions, DI referrals, and arrival/discharge times of the patients. We compared outcomes of patients seen by the RABT to historical site data of patients with a discharge diagnosis of LBP from the ED. We actively sought feedback from physicians, nurses, and the leadership group to ensure that we identified unintended consequences or near-misses early on. We reviewed interim data such as LOS and average time-to-assessment, to identify areas for improvement. This data and feedback were addressed via bimonthly PDSA cycles. We also administered patient and staff satisfaction surveys before and after site implementation of the pathway to develop an understanding of patient and staff thoughts and experiences with the service model. Results: We studied these outcomes in 44 patients exposed to our RABT implementation. Patients who saw a physiotherapist prior to the physician had shorter median ED LOS (3.2 vs. 4.0 hours), lower diagnostic imaging rates (36.4% vs. 49.4%) and less opioid prescribing (31.8% vs. 49.2%). No patients returned to the ED within 72 hours post evaluation, compared to the 7.6% historical recidivism. Not all patients were seen by a pharmacist. When performing a subgroup analysis of patients seen by both a pharmacist and physiotherapist prior to physician, opioid prescriptions were found to drop significantly from a baseline of 49.2% to 16.7%. Advice and Lessons Learned: Valuable learnings from the pilot include: As described above, the physiotherapist and pharmacist must be experienced and confident to be successful in the ED setting. Selection of the appropriate clinicians is crucial to achieving results, and given this is a new area of practice for many physiotherapists, a proper orientation to the setting is required. The combination of a physiotherapist and pharmacist had the largest impact on study outcomes, further confirming the need for a multidisciplinary approach to ED patient care. An “ED toolkit” can greatly facilitate service implementation for future sites, and this was developed to facilitate implementation of the RABT at another ED within the city. The toolkit consisted of items such as resources, workflows, patient handouts, sample documentation and promotional materials to increase awareness. Service hours may need some realignment with patient demand and should be geared towards minimizing service disruptions. Ideally, the physiotherapist and pharmacist would work similar hours to maximize the amount of patients able to be seen. Regular PDSA cycles to review interim data and address operational issues increases the likelihood of success by ensuring the pathway evolves to fit the contextual needs of the site. Reviewing early results motivates the team to continue to utilize the pathway. Reviewing practice issues allows clinicians to improve the care provided. One significant unintended consequence was the increase in ED LOS for patients who were referred to PT/pharmacy following physician assessment. In addition, this subgroup did not show significant reductions in opioid prescriptions or DI referrals. RABT referrals were subsequently restricted to before the physician only.

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)0,004
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,022

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0020,001
Science ouverte0,0010,005
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

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 Opus0,024
Tête enseignante GPT0,289
Écart entre enseignants0,266 · 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'étudeSans objet
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

Citations0
Publié2020
Routes d'admission2
Résumé présentoui

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Même revueCanadian Journal of Emergency NursingMême sujetMusculoskeletal pain and rehabilitationTravaux en français237 207