How a nursing navigation role enhances patient recovery across Orthopedic Integrated Pathways
Notice bibliographique
Résumé
Introduction: Our academic urban institution developed an integrated care pathway that has significantly improved patient transitions to outpatient rehabilitation after total joint replacement (TJR) and as patients requested, supports their earlier discharge from a hospital to home to recover. Early recovery at home has identified a need for care navigation, particularly for patients with a higher complexity of medical and social issues. Implementation of a nursing role for care navigation into our existing integrated pathways would provide support for patients receiving TJR surgery ensuring better coordination of services in their community. Aims, Objectives and Methodology: Our objective was to evaluate an IC nursing lead (ICL) role embedded in our existing orthopedic TJR integrated pathway to support “extended” care patients to safely discharge home. Process mapping sessions conducted in 2019-20, that included key stakeholders and patient partners, identified gaps in communication and care coordination before and after surgery affecting overall patient experience and surgical recovery. “Extended” care patients were identified by the orthopedic care team using a standardized referral form developed in conjunction with the ICL. Patients were contacted 1-2 weeks pre-surgery and supported at various pre-operative time points with care coordination. An in-person visit with the patient occurred on the day of surgery and prior to discharge from hospital. A standard 24-hr post-op phone call was completed once the patient returned home. The ICL could be engaged by the patient or care team at any point across the TJR journey (between consent and day of surgery, acute inpatient stay, and up to 90 days post-surgery. Key Findings: The ICL role, piloted in March 2022, started with gradual enrolment of patients across 2 arthroplasty surgeons. By August 2022, all 6 arthroplasty care teams (surgeons, physiotherapist practitioners, fellows, physician assistants and admins) were committed to this model of care. ICL patients were 41% male and 59% female ranging in age from 50-92 (mean age of 78). Inpatient pts represented 94% (ALOS 1-2 days). Discharge (DC) disposition patterns aligned with existing best practice guidelines. Discharge coordination made up the bulk of support (82%) provided by the ICL. Other key themes and interventions included emotional reassurance (32%), education/expectation setting (30%), information on community resources and services (18%), arrangement of homecare (6%), other miscellaneous questions (5%). The average time spent for care coordination was 90 mins per patient over the 90 day period. Qualitative report from ICL patients revealed improved patient experience with the degree of detail and support provided. "I'm so glad I can call you. I don't always know who to talk to when I have questions". "It's so good to have someone pick up the phone when I need an answer when everything is closed". Conclusions: Early findings highlight the benefits of an ICL in improving care quality of patients undergoing TJR and enabling coordinated transitions post discharge from hospital through to their recovery at home. As hospital stays for TJR becoming exceedingly shorter this type of role becomes even more critical, especially with increased patient needs in the aftermath of COVID-19.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».